Yun-Chun Chen

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I am a Ph.D. candidate in Computer Science at the University of Toronto, advised by Alec Jacobson. My research interests are in 3D vision, graphics, geometry and generative AI.

This summer, I interned at Adobe Research with Vova Kim, Matheus Gadelha and Zhiqin Chen. In 2022, I interned at Adobe Research with Vova Kim and Noam Aigerman. In 2021, I was an intern in the NVIDIA Seattle Robotics lab, working with Dieter Fox, Adithya Murali, and Balakumar Sundaralingam.

Prior to my Ph.D., I worked with Ming-Hsuan Yang, Jia-Bin Huang, and Yen-Yu Lin. I received a B.S. in Electrical Engineering from National Taiwan University in 2018.

I am actively looking for internship opportunities to start in 2024. Please contact me if you have any leads!


Selected Publications

Neural Progressive Meshes
[Paper] [Poster] [Slides]
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2022
Featured Paper Presentation
[Paper] [Project page] [Baseline code] [Data generation code] [Dataset] [Poster] [Slides] [Twitter]
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
European Conference on Computer Vision (ECCV), 2022
Oral Presentation
[Paper] [Project page] [Code] [Video] [Poster] [Twitter]
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
RSS 2022 Workshop on Implicit Representations for Robotic Manipulation, 2022
Spotlight Talk
[Paper] [Video] [Slides] [Twitter]
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
[Paper] [Project page] [Video] [Poster] [Slides] [Twitter]
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
RSS 2021 Workshop on Visual Learning and Reasoning for Robotics, 2021 Spotlight Talk
ICML 2021 Workshop on Human in the Loop Learning, 2021
[Paper] [Project page] [Video] [Twitter]
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2021
[Paper] [Project page] [Code] [Slides]
Self-Attentive 3D Human Pose and Shape Estimation from Videos
Computer Vision and Image Understanding (CVIU), 2021
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search
European Conference on Computer Vision (ECCV), 2020
[Paper] [Project page] [GitHub] [Colab] [Highlight video] [Highlight slides] [Full video] [Full slides]
Learning to Learn in a Semi-Supervised Fashion
European Conference on Computer Vision (ECCV), 2020
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
arXiv preprint arXiv:2002.09274
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
IEEE International Conference on Computer Vision (ICCV), 2019
[Paper] [Slides] [Poster]
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
[Paper] [Project page] [Code] [Slides] [Poster]
Learning Resolution-Invariant Deep Representations for Person Re-Identification
AAAI Conference on Artificial Intelligence (AAAI), 2019
Oral Presentation
[Paper] [Slides] [Poster]
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Asian Conference on Computer Vision (ACCV), 2018
[Paper] [Project page] [Code] [Poster]