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Table of Links Abstract and 1. Introduction 2 Literature Review 3 Approach 3.1 Problem Structure & Dynamic Scaffolding 3.2 Informal Assessment & Feedback 3.3 Pair Programming Dyad 3.4 Course Structure 4 Discussion 5 Conclusion 6 Acknowledgements and References 2 Literature Review There has been a significant amount of research into using pair programming for educational purposes. The popularization of pair programming largely started by Extreme Programming Explained (Beck, 2000). It promoted pair programming, among other techniques, as a way of producing high quality code faster than conventional methods. Williams and Upchurch (2001) combined professionals with advanced undergraduates and found that “Experimental results show pair-programming pairs develop better quality code faster with only a minimal increase in pre-release programmer hours.” Further, they noted that the programmers generally found that pair programming was more enjoyable than programming alone. Hannay et al. (2009) studied the effect of using pair programming in a CS1 course with 600 students. Students did their work with pair programming, alternating driver and navigator roles. The results were better quality programs and a significantly higher rate of completion for the course among those that participated in pair programming. Williams and Upchurch (2001) had students use pair programming for their work in a web based programming course. The results of an anonymous survey conclude that the 74% of students thought that they could solve any problem with their partner and 84% believed they learned faster because of their partner. Though there are some mixed results among studies as well. McDowell et al. (2003) saw no improvement in midterm or programming scores, but did see an improvement under some “holistic” scores. A large randomized trial of 1,530 undergraduates found that pair programming was virtually unrelated to all measured outcomes. However, pair programming did appear to hurt the grades, success, and likelihood of White students and had no effect on any other demographic (Bowman et al., 2020). A study of persistence of CS students suggests that pair programming might improve the persistence of women in CS though the results were not statistically significant Werner et al. (2004). Hannay et al. (2009) conducted a meta-analysis of 18 studies concluded that has an overall positive impact on software quality and time, but did require more total effort by the programmers involved. The studies were on a mix of educational and professional teams. However, they noted that pair programming offers a substantial benefit for junior developers, with their code correctness increasing 73% on regular programs and 149% on more complex programs. Recently another meta-study was conducted on all the published articles on pair programming for higher education from 2010 to 2020. The analysis contained 61 articles and contained some important conclusions and observations. First, pair programming is generally beneficial for student learning, particularly for inexperienced students. Second, pair programming is generally difficult to apply successfully in the classroom. They noted a lack of research on effective instructional design for pair programming. Most research on instructional design is on how to select the pairs, though the results are mixed. In particular, Hawlitschek et al. (2022) identified that the problem with pair programming is when two weak students are paired together without much guidance. Author:
(1) J. Walker Orr, Electrical Engineering and Computer Science, George Fox University, Newberg, OR, 97132, USA (jorr@georgefox.edu). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Literature Review 2 Literature Review 3 Approach 3 Approach 3.1 Problem Structure & Dynamic Scaffolding 3.1 Problem Structure & Dynamic Scaffolding 3.2 Informal Assessment & Feedback 3.2 Informal Assessment & Feedback 3.3 Pair Programming Dyad 3.3 Pair Programming Dyad 3.4 Course Structure 3.4 Course Structure 4 Discussion 4 Discussion 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References 2 Literature Review There has been a significant amount of research into using pair programming for educational purposes. The popularization of pair programming largely started by Extreme Programming Explained (Beck, 2000). It promoted pair programming, among other techniques, as a way of producing high quality code faster than conventional methods. Williams and Upchurch (2001) combined professionals with advanced undergraduates and found that “Experimental results show pair-programming pairs develop better quality code faster with only a minimal increase in pre-release programmer hours.” Further, they noted that the programmers generally found that pair programming was more enjoyable than programming alone. Hannay et al. (2009) studied the effect of using pair programming in a CS1 course with 600 students. Students did their work with pair programming, alternating driver and navigator roles. The results were better quality programs and a significantly higher rate of completion for the course among those that participated in pair programming. Williams and Upchurch (2001) had students use pair programming for their work in a web based programming course. The results of an anonymous survey conclude that the 74% of students thought that they could solve any problem with their partner and 84% believed they learned faster because of their partner. Though there are some mixed results among studies as well. McDowell et al. (2003) saw no improvement in midterm or programming scores, but did see an improvement under some “holistic” scores. A large randomized trial of 1,530 undergraduates found that pair programming was virtually unrelated to all measured outcomes. However, pair programming did appear to hurt the grades, success, and likelihood of White students and had no effect on any other demographic (Bowman et al., 2020). A study of persistence of CS students suggests that pair programming might improve the persistence of women in CS though the results were not statistically significant Werner et al. (2004). Hannay et al. (2009) conducted a meta-analysis of 18 studies concluded that has an overall positive impact on software quality and time, but did require more total effort by the programmers involved. The studies were on a mix of educational and professional teams. However, they noted that pair programming offers a substantial benefit for junior developers, with their code correctness increasing 73% on regular programs and 149% on more complex programs. Recently another meta-study was conducted on all the published articles on pair programming for higher education from 2010 to 2020. The analysis contained 61 articles and contained some important conclusions and observations. First, pair programming is generally beneficial for student learning, particularly for inexperienced students. Second, pair programming is generally difficult to apply successfully in the classroom. They noted a lack of research on effective instructional design for pair programming. Most research on instructional design is on how to select the pairs, though the results are mixed. In particular, Hawlitschek et al. (2022) identified that the problem with pair programming is when two weak students are paired together without much guidance. Author: (1) J. Walker Orr, Electrical Engineering and Computer Science, George Fox University, Newberg, OR, 97132, USA (jorr@georgefox.edu). Author: Author: (1) J. Walker Orr, Electrical Engineering and Computer Science, George Fox University, Newberg, OR, 97132, USA (jorr@georgefox.edu). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

The Impact of Pair Programming on Computer Science Education

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Table of Links Abstract and 1 Introduction 2 Related Work 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 5 The DeepSea System 5.1 Map View
5.2 Core View 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.3 Limitations 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 3 METHODOLOGY In this paper, we followed Sedlmair et al.’s methodological framework for design studies [43]. Each phase of the study resulted in a contribution, namely: (1) a characterization of the problem domain via design challenges and user tasks; (2) a novel and validated visualization design; and (3) reflections on the process and lessons learned for improving visualization and interaction design guidelines. We connect these phases throughout the paper by illustrating how our synthesized challenges and tasks informed DeepSee’s design and how DeepSee’s success in addressing the challenges and tasks is reflected in our evaluation. To the best of our knowledge, the problem we describe in Sect. 4 is unique to our collaborators and has not been addressed by other researchers so far. Thus, we posit that a design study based on active participation from collaborators in both the design and evaluation phases is necessary to successfully support domain experts in their highly specialized tasks. We began by establishing a close collaboration between visualization researchers and domain experts, to properly assess the impact of our approach on solving a real-world problem. In line with prior design studies [15, 18, 33], our team comprised all authors and included visualization experts in human-centered computing, interaction design, scientific data visualization and art, as well as scientists and researchers with expertise in environmental microbiology, geochemistry and geology. We developed DeepSee through several user-centered design methodologies including contextual inquiry, mixed-fidelity prototyping, user testing, and design iteration. Over an intensive ten-week period, we worked closely together to develop a shared understanding of the importance and aims of their scientific research (Sect. 4.1), characterize data and workflows (Sect. 4.2), and identify data visualization challenges and user tasks (Sect. 4.3) that would guide our system design. Our process started with contextual inquiry, combining observation and interviews with our domain collaborators to deeply understand their existing tools and workflows, discuss unmet needs and pain points, and identify key technical constraints. After two weeks, we began mixed-fidelity prototyping, starting with paper prototypes, wireframes and small interactive prototypes in code, before moving on to progressively higher-fidelity prototypes. These prototypes were iteratively discussed with the science team over the next seven weeks through semi-structured user testing. Using these prototypes as stimuli, we deepened our understanding of the problem space through on-going contextual inquiry. During this time, systems diagrams and conceptual workflows were also created to help us validate our understanding of the problem space in collaboration with the science team. As a result of this approach, we progressively uncovered insights that guided each subsequent design iteration, leading us to generate a working prototype described in Sect. 5 that addressed our collaborators’ tasks. DeepSee was then deployed in the wild [43] to assess whether our approach solves a real problem – bridging gaps in supporting visual analysis of samples during field research. Deploying DeepSee with the same experts that we designed with allowed us to assess whether domain experts are indeed helped by the new solution, as well as reflect on and refine principles for visualization and interaction design guidelines [43]. Specifically, we investigated the impact of introducing data visualizations into both planning before fieldwork as well as tactical decision making in the field. We learned that visualizing data at multiple scales, integrating 2D and 3D data in the same interface, and modularizing visualizations positively impacted scientists planning workflows the most, and that additional support for integrating data on the fly could better enable in-situ decision making. Methodological details of our evaluation and how we synthesized these lessons learned are provided in Sect. 7. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu);
(2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu);
(3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu);
(4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com);
(5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu);
(6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu);
(7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu);
(8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu);
(9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org);
(10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org);
(11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org);
(12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu);
(13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu);
(14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu);
(15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu);
(16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov);
(17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 4.3 Design Challenges and User Tasks 5 The DeepSea System 5 The DeepSea System 5.1 Map View 5.2 Core View 5.1 Map View 5.2 Core View 5.3 Interpolation View and 5.4 Implementation 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.2 Expert Interviews 7.3 Limitations 7.3 Limitations 7.4 Lessons Learned 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 8 Conclusions and Future Work, Acknowledgments, and References 3 METHODOLOGY In this paper, we followed Sedlmair et al.’s methodological framework for design studies [43]. Each phase of the study resulted in a contribution, namely: (1) a characterization of the problem domain via design challenges and user tasks; (2) a novel and validated visualization design; and (3) reflections on the process and lessons learned for improving visualization and interaction design guidelines. We connect these phases throughout the paper by illustrating how our synthesized challenges and tasks informed DeepSee’s design and how DeepSee’s success in addressing the challenges and tasks is reflected in our evaluation. To the best of our knowledge, the problem we describe in Sect. 4 is unique to our collaborators and has not been addressed by other researchers so far. Thus, we posit that a design study based on active participation from collaborators in both the design and evaluation phases is necessary to successfully support domain experts in their highly specialized tasks. We began by establishing a close collaboration between visualization researchers and domain experts, to properly assess the impact of our approach on solving a real-world problem. In line with prior design studies [15, 18, 33], our team comprised all authors and included visualization experts in human-centered computing, interaction design, scientific data visualization and art, as well as scientists and researchers with expertise in environmental microbiology, geochemistry and geology. We developed DeepSee through several user-centered design methodologies including contextual inquiry, mixed-fidelity prototyping, user testing, and design iteration. Over an intensive ten-week period, we worked closely together to develop a shared understanding of the importance and aims of their scientific research (Sect. 4.1), characterize data and workflows (Sect. 4.2), and identify data visualization challenges and user tasks (Sect. 4.3) that would guide our system design. Our process started with contextual inquiry, combining observation and interviews with our domain collaborators to deeply understand their existing tools and workflows, discuss unmet needs and pain points, and identify key technical constraints. After two weeks, we began mixed-fidelity prototyping, starting with paper prototypes, wireframes and small interactive prototypes in code, before moving on to progressively higher-fidelity prototypes. These prototypes were iteratively discussed with the science team over the next seven weeks through semi-structured user testing. Using these prototypes as stimuli, we deepened our understanding of the problem space through on-going contextual inquiry. During this time, systems diagrams and conceptual workflows were also created to help us validate our understanding of the problem space in collaboration with the science team. As a result of this approach, we progressively uncovered insights that guided each subsequent design iteration, leading us to generate a working prototype described in Sect. 5 that addressed our collaborators’ tasks. DeepSee was then deployed in the wild [43] to assess whether our approach solves a real problem – bridging gaps in supporting visual analysis of samples during field research. Deploying DeepSee with the same experts that we designed with allowed us to assess whether domain experts are indeed helped by the new solution, as well as reflect on and refine principles for visualization and interaction design guidelines [43]. Specifically, we investigated the impact of introducing data visualizations into both planning before fieldwork as well as tactical decision making in the field. We learned that visualizing data at multiple scales, integrating 2D and 3D data in the same interface, and modularizing visualizations positively impacted scientists planning workflows the most, and that additional support for integrating data on the fly could better enable in-situ decision making. Methodological details of our evaluation and how we synthesized these lessons learned are provided in Sect. 7. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Authors: Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu).

Making Field Research Easier with DeepSee’s Data Visualization

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Author:
(1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com);
(2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com);
(3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com);
(4) Christoph Bregler, Google Research USA (Email: bregler@google.com). Table of Links Abstract and Introduction
Background and Related Work
Method
Results and Failure Cases
Limitations and Conclusion, Acknowledgements and References ABSTRACT Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suited to video conferencing due to the consistent subject matter (front-facing humans), however training-based approaches are somewhat pointless for entertainment videos where varied subjects (spaceships, monsters, etc.) may appear only a few times in a single movie – if a method of creating ground truth for training exists, just use that method to produce the desired mattes. We introduce a training-free high quality neural matte extraction approach that specifically targets the assumptions of visual effects production. Our approach is based on the deep image prior, which optimizes a deep neural network to fit a single image, thereby providing a deep encoding of the particular image. We make use of the representations in the penultimate layer to interpolate coarse and incomplete "trimap" constraints. Videos processed with this approach are temporally consistent. The algorithm is both very simple and surprisingly effective. CCS CONCEPTS • Computing methodologies → Image processing; • Applied computing → Media arts. KEYWORDS Alpha matting, deep learning, visual effects. ACM Reference Format: Sharif Elcott, J.P. Lewis, Nori Kanazawa, and Christoph Bregler. 2022. TrainingFree Neural Matte Extraction for Visual Effects. In SIGGRAPH Asia 2022 Technical Communications (SA ’22 Technical Communications), December 6–9, 2022, Daegu, Republic of Korea. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3550340.3564230 1 INTRODUCTION Alpha matte extraction refers to the underconstrained inverse problem of finding the unknown translucency or coverage 𝛼 of a foreground object [Wang and Cohen 2007]. Matte extraction is widely used to provide alternate backgrounds for video meetings, as well as to produce visual effects (VFX) for movies, television, and social media. However, it is not always recognized in the research literature that these two applications have significantly different requirements. This paper introduces a neural matte extraction method specifically addressed to the assumptions and requirements of VFX. pecifically addressed to the assumptions and requirements of VFX. Matting for video calls requires real-time performance and assumes a single class of subject matter, front-facing humans. This is a "train once and use many times" situation for which it is possible and advantageous to obtain training data. Video call matting often assumes a fixed camera, and may require a "clean plate" image of the room without the participant. On the contrary, VFX production has the following assumptions and requirements: • Diverse and often rare ("one-off") subject matter. For example the youtube video [Godzilla vs. Cat 2021] (30M views) involves mattes of a cat, ships, and wreckage. A physical prop such as a crashed alien spaceship might only be used in a few seconds in a single movie, never to be seen again. Thus, gathering a ground-truth training dataset for deep learning is often pointless: if there is a method to generate the ground-truth mattes for training, just use this method to produce the desired mattes – no need to train a model! • Visual effects frequently involves moving cameras as well as extreme diversity of moving backgrounds. For example, an actor might be filmed as they run or travel in a vehicle any place on earth, or on set with extraterrestrial props in the near background. Thus, even for the major case of repeated subject matter (humans), gathering a representative training dataset is more challenging than in the case of video calls. • Real-time performance is not required. Instead, the on-set filming time is to be minimized, due to the combined cost of the actors (sometimes with 7-figure salaries) and movie crew. It is often cheaper to employ an artist to work days to "fix it in post" rather than spend a few minutes on-set (with actors and crew waiting) to address an issue. • Clean-plates are not a desirable approach for matting, and are often not possible. With moving cameras, a motion control rig is required to obtain a clean plate. The indoor use of motion control for clean plates is expensive due to the previous principle (minimizing on-set time). Moreover, this approach is generally not feasible outdoors, for reasons including background movement (e.g. plants moved by wind) and changing lighting (moving clouds blocking the sun). Our solution, a matte extraction approach employing the deep image prior [Ulyanov et al. 2018], addresses these requirements. It has the following characteristics and contributions: • It is a deep neural network matte extraction method targeted towards the requirements of VFX rather than video meetings. To our knowledge, this is the first deep neural matte extraction method that requires neither training data nor expensive on-set image capture to support the matting process (i.e. clean plates or greenscreens). • It relies exclusively on the given image or video along with coarse "trimaps" (Fig. 1) that are easily created during post production using existing semi-automatic tools. Professional software such as Nuke or After Effects is typically used, however a familiar example is the "Select Subject" tool in Photoshop [Adobe 2018], followed by dilation/erosion and automated with "Actions". • It does not require a clean plate, thus allowing extractions from outdoor footage. • It does not require greenscreens, yet can produce detailed high quality mattes even when the foreground and background subject have similar colors (green hair in Fig. 1). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com); (2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com); (3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com); (4) Christoph Bregler, Google Research USA (Email: bregler@google.com). Author: Author: (1) Sharif Elcott, equal contribution of Google Japan (Email: selcott@google.com); (2) J.P. Lewis, equal contribution of Google Research USA (Email: jplewis@google.com); (3) Nori Kanazawa, Google Research USA (Email: kanazawa@google.com); (4) Christoph Bregler, Google Research USA (Email: bregler@google.com). Table of Links Abstract and Introduction Background and Related Work Method Results and Failure Cases Limitations and Conclusion, Acknowledgements and References Abstract and Introduction Abstract and Introduction Background and Related Work Background and Related Work Method Method Results and Failure Cases Results and Failure Cases Limitations and Conclusion, Acknowledgements and References Limitations and Conclusion, Acknowledgements and References ABSTRACT Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suited to video conferencing due to the consistent subject matter (front-facing humans), however training-based approaches are somewhat pointless for entertainment videos where varied subjects (spaceships, monsters, etc.) may appear only a few times in a single movie – if a method of creating ground truth for training exists, just use that method to produce the desired mattes. We introduce a training-free high quality neural matte extraction approach that specifically targets the assumptions of visual effects production. Our approach is based on the deep image prior, which optimizes a deep neural network to fit a single image, thereby providing a deep encoding of the particular image. We make use of the representations in the penultimate layer to interpolate coarse and incomplete "trimap" constraints. Videos processed with this approach are temporally consistent. The algorithm is both very simple and surprisingly effective. CCS CONCEPTS • Computing methodologies → Image processing; • Applied computing → Media arts. KEYWORDS Alpha matting, deep learning, visual effects. ACM Reference Format: Sharif Elcott, J.P. Lewis, Nori Kanazawa, and Christoph Bregler. 2022. TrainingFree Neural Matte Extraction for Visual Effects. In SIGGRAPH Asia 2022 Technical Communications (SA ’22 Technical Communications), December 6–9, 2022, Daegu, Republic of Korea. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3550340.3564230 1 INTRODUCTION Alpha matte extraction refers to the underconstrained inverse problem of finding the unknown translucency or coverage 𝛼 of a foreground object [Wang and Cohen 2007]. Matte extraction is widely used to provide alternate backgrounds for video meetings, as well as to produce visual effects (VFX) for movies, television, and social media. However, it is not always recognized in the research literature that these two applications have significantly different requirements. This paper introduces a neural matte extraction method specifically addressed to the assumptions and requirements of VFX. pecifically addressed to the assumptions and requirements of VFX. Matting for video calls requires real-time performance and assumes a single class of subject matter, front-facing humans. This is a "train once and use many times" situation for which it is possible and advantageous to obtain training data. Video call matting often assumes a fixed camera, and may require a "clean plate" image of the room without the participant. On the contrary, VFX production has the following assumptions and requirements: • Diverse and often rare ("one-off") subject matter. For example the youtube video [Godzilla vs. Cat 2021] (30M views) involves mattes of a cat, ships, and wreckage. A physical prop such as a crashed alien spaceship might only be used in a few seconds in a single movie, never to be seen again. Thus, gathering a ground-truth training dataset for deep learning is often pointless: if there is a method to generate the ground-truth mattes for training, just use this method to produce the desired mattes – no need to train a model! • Visual effects frequently involves moving cameras as well as extreme diversity of moving backgrounds. For example, an actor might be filmed as they run or travel in a vehicle any place on earth, or on set with extraterrestrial props in the near background. Thus, even for the major case of repeated subject matter (humans), gathering a representative training dataset is more challenging than in the case of video calls. • Real-time performance is not required. Instead, the on-set filming time is to be minimized, due to the combined cost of the actors (sometimes with 7-figure salaries) and movie crew. It is often cheaper to employ an artist to work days to "fix it in post" rather than spend a few minutes on-set (with actors and crew waiting) to address an issue. • Clean-plates are not a desirable approach for matting, and are often not possible. With moving cameras, a motion control rig is required to obtain a clean plate. The indoor use of motion control for clean plates is expensive due to the previous principle (minimizing on-set time). Moreover, this approach is generally not feasible outdoors, for reasons including background movement (e.g. plants moved by wind) and changing lighting (moving clouds blocking the sun). Our solution, a matte extraction approach employing the deep image prior [Ulyanov et al. 2018], addresses these requirements. It has the following characteristics and contributions: • It is a deep neural network matte extraction method targeted towards the requirements of VFX rather than video meetings. To our knowledge, this is the first deep neural matte extraction method that requires neither training data nor expensive on-set image capture to support the matting process (i.e. clean plates or greenscreens). • It relies exclusively on the given image or video along with coarse "trimaps" (Fig. 1) that are easily created during post production using existing semi-automatic tools. Professional software such as Nuke or After Effects is typically used, however a familiar example is the "Select Subject" tool in Photoshop [Adobe 2018], followed by dilation/erosion and automated with "Actions". • It does not require a clean plate, thus allowing extractions from outdoor footage. • It does not require greenscreens, yet can produce detailed high quality mattes even when the foreground and background subject have similar colors (green hair in Fig. 1). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Training-Free Neural Matte Extraction for Visual Effects: Abstract and Introduction

Authors:
(1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA;
(2) Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA;
(3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA;
(4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy;
(5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA;
(6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA;
(7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. Table of Links Abstract and I Introduction II Methods III Results IV Discussion V. Conclusion, Acknowledgment, and References IV. DISCUSSION In this paper, we evaluated a deep-learning model to predict the weight distribution (i.e., stance interpolation factor) of a user wearing a lower-limb exoskeleton during several walking conditions; we compared the exoskeleton controller performance when this stance interpolation factor was estimated with our deep-learning model (αˆ ) and measured with ground truth values (treadmill force plates or FSR sensor pads, α). This work highlights the viability and limitations of using deep-learning predictions to detect changes in the gait state using only joint kinematics information without ground reaction sensors. Utilizing a history of kinematic data was shown to enhance the model’s accuracy (Fig. 3 and Tab. I) in comparison to using instantaneous values, as seen in a similar study [14]. These findings show the advantage of utilizing additional temporal data from previous time steps, enhancing the network’s performance in both training and testing phases. Importantly, including additional data did not significantly alter the model’s prediction speed; we observed comparable prediction times when using a history of kinematic data with respect to the instantaneous value predictions. As a result, the model was implemented to achieve real-time performance. This is crucial for its use in the control of an exoskeleton, as predictions need to be faster than the main control loop. Achieving an average prediction time of 0.57 ms ensured the real-time usability of the system, as the main control loop runs usually significantly slower than 1 ms on lower-limb exoskeletons (e.g., 3 ms for the exoskeleton controller in our study). It is noteworthy that, without using TensorFlow Lite (i.e., employing the classic TensorFlow library), the average prediction time with a history of kinematic data is 67.1 ± 13.5 ms, which cannot be used in most real-time applications. Furthermore, the proposed method provides accurate predictions of the stance interpolation factor for previous and unseen users (Fig. 3 and Tab. I), demonstrating another aspect of its usability. Requiring training data for every exoskeleton user is not convenient or realistic, particularly in physical rehabilitation as a patient’s time spent receiving therapy must be prioritized. In the context of machine learning for robotic control, using the predictions in a closed loop can affect the performance due to error propagation in real time [20]. Therefore, it was critical to evaluate the closed-loop performance of our system, using several conditions. We demonstrated that the deep-learning approach produces a similar stance interpolation factor compared to treadmill force plates during walking with haptic transparent control; these findings generalize for walking speeds between 0.14 and 0.47 m/s (Fig. 4). To characterize the effect of the deep-learning predictions on the performance of the exoskeleton controller, we assessed both haptic rendering of nonzero impedance and haptic transparent modes during treadmill walking. Moreover, we evaluated the extent to which these results generalize to overground walking. During treadmill walking, mean interaction torque errors were similar between deep-learning predictions and treadmill force plate measurements. Evaluating the performance of the controller during more naturalistic, overground walking, the deep-learning predictions also facilitated similar interaction torque error profiles (Fig. 7A) and hip and knee kinematics (Fig. 7B). While the overall performance of the exoskeleton controller was comparable when implementing our deeplearning predictions and ground reaction sensor values, we did observe a delay-like effect in the stance interpolation factor estimation which likely resulted in some performance discrepancies at specific phases of the gait cycle. Specifically, we observed a longer stance duration when walking with the deep-learning predictions at slower speeds (Fig. 4); this was associated with a longer transition between left and right single stance (i.e., double stance to swing). During treadmill and overground walking, at the knee joint, we observed higher interaction torque error at the beginning of the swing phase (around 60-70% of the cycle, Fig. 6A) with the deep-learning predictions. This increase in interaction torque error was more noticeable during overground walking, and also observed at the hip joint. In this condition, the use of crutches may have promoted additional lateral movement which was not measured by the exoskeleton [21]. For both treadmill and overground walking, the discrepancies in interaction torque errors relative to their respective ground truths could be due to the deep-learning model requiring users to change their kinematics before the stance interpolation factor changes. This is different from using force sensors, which can detect changes in weight distribution independent of kinematics recorded by the exoskeleton. One way to improve the detection of changes in weight distribution in the deep-learning model is to incorporate the frontal plane IMU angle of the backpack in addition to the sagittal plane angles from the backpack and joint encoders. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (2) Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy; (5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA; (6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. Authors: Authors: (1) Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (2) Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (3) Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; (4) Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pontedera, Italy and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy; (5) Matthew R. Short, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA; (6) Kevin Lynch2, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; (7) Jose L. Pons, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA, Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA and Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA. Table of Links Abstract and I Introduction Abstract and I Introduction II Methods II Methods III Results III Results IV Discussion IV Discussion V. Conclusion, Acknowledgment, and References V. Conclusion, Acknowledgment, and References IV. DISCUSSION In this paper, we evaluated a deep-learning model to predict the weight distribution (i.e., stance interpolation factor) of a user wearing a lower-limb exoskeleton during several walking conditions; we compared the exoskeleton controller performance when this stance interpolation factor was estimated with our deep-learning model (αˆ ) and measured with ground truth values (treadmill force plates or FSR sensor pads, α). This work highlights the viability and limitations of using deep-learning predictions to detect changes in the gait state using only joint kinematics information without ground reaction sensors. Utilizing a history of kinematic data was shown to enhance the model’s accuracy (Fig. 3 and Tab. I) in comparison to using instantaneous values, as seen in a similar study [14]. These findings show the advantage of utilizing additional temporal data from previous time steps, enhancing the network’s performance in both training and testing phases. Importantly, including additional data did not significantly alter the model’s prediction speed; we observed comparable prediction times when using a history of kinematic data with respect to the instantaneous value predictions. As a result, the model was implemented to achieve real-time performance. This is crucial for its use in the control of an exoskeleton, as predictions need to be faster than the main control loop. Achieving an average prediction time of 0.57 ms ensured the real-time usability of the system, as the main control loop runs usually significantly slower than 1 ms on lower-limb exoskeletons (e.g., 3 ms for the exoskeleton controller in our study). It is noteworthy that, without using TensorFlow Lite (i.e., employing the classic TensorFlow library), the average prediction time with a history of kinematic data is 67.1 ± 13.5 ms, which cannot be used in most real-time applications. Furthermore, the proposed method provides accurate predictions of the stance interpolation factor for previous and unseen users (Fig. 3 and Tab. I), demonstrating another aspect of its usability. Requiring training data for every exoskeleton user is not convenient or realistic, particularly in physical rehabilitation as a patient’s time spent receiving therapy must be prioritized. In the context of machine learning for robotic control, using the predictions in a closed loop can affect the performance due to error propagation in real time [20]. Therefore, it was critical to evaluate the closed-loop performance of our system, using several conditions. We demonstrated that the deep-learning approach produces a similar stance interpolation factor compared to treadmill force plates during walking with haptic transparent control; these findings generalize for walking speeds between 0.14 and 0.47 m/s (Fig. 4). To characterize the effect of the deep-learning predictions on the performance of the exoskeleton controller, we assessed both haptic rendering of nonzero impedance and haptic transparent modes during treadmill walking. Moreover, we evaluated the extent to which these results generalize to overground walking. During treadmill walking, mean interaction torque errors were similar between deep-learning predictions and treadmill force plate measurements. Evaluating the performance of the controller during more naturalistic, overground walking, the deep-learning predictions also facilitated similar interaction torque error profiles (Fig. 7A) and hip and knee kinematics (Fig. 7B). While the overall performance of the exoskeleton controller was comparable when implementing our deeplearning predictions and ground reaction sensor values, we did observe a delay-like effect in the stance interpolation factor estimation which likely resulted in some performance discrepancies at specific phases of the gait cycle. Specifically, we observed a longer stance duration when walking with the deep-learning predictions at slower speeds (Fig. 4); this was associated with a longer transition between left and right single stance (i.e., double stance to swing). During treadmill and overground walking, at the knee joint, we observed higher interaction torque error at the beginning of the swing phase (around 60-70% of the cycle, Fig. 6A) with the deep-learning predictions. This increase in interaction torque error was more noticeable during overground walking, and also observed at the hip joint. In this condition, the use of crutches may have promoted additional lateral movement which was not measured by the exoskeleton [21]. For both treadmill and overground walking, the discrepancies in interaction torque errors relative to their respective ground truths could be due to the deep-learning model requiring users to change their kinematics before the stance interpolation factor changes. This is different from using force sensors, which can detect changes in weight distribution independent of kinematics recorded by the exoskeleton. One way to improve the detection of changes in weight distribution in the deep-learning model is to incorporate the frontal plane IMU angle of the backpack in addition to the sagittal plane angles from the backpack and joint encoders. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Weight Distribution Estimation in Lower-Limb Exoskeletons via Deep Learning: Discussion

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