Human-centric image/video synthesis has been intensely researched in computer vision, with the wide application domains such as human motion transfer, virtual try-on, virtual reality, and human-computer interaction. Developing solutions to understand the human-centric image/video synthesis in the practicable scenarios, regarded as one of the most fundamental problems in computer vision, could have a crucial impact in many industrial application domains. Those bring great convenience for the public. However, there exists a large gap between the human-centric synthesis technique and its carrying out applied in the practical scenarios. What is needed by the real-life applications? What is achievable based on modern computer vision techniques? Those all raise the researchers’ attentions and discussions. More human image synthesis, virtual try-on, and 3D graphic analysis research advances are urgently expected for advanced human-centric synthesis. For example, the 2D/3D clothes virtual try-on simulation system that seamlessly fits various clothes into 3D human body shape has attracted numerous commercial interests. The human motion synthesis and prediction can bridge the virtual and real worlds, such as, simulating virtual characters to mimic the human behaviors, empowering robotics more intelligent interactions with human by enabling causal inferences for human activities. The goal of this workshop is to allow researchers from the fields of human-centric image/video synthesis to present their progress, communication and co-develop novel ideas that potentially shape the future of this area and further advance the performance and applicability of correspondingly built systems in real-world conditions. This workshop is designed to build up consensus on the emerging topic of the human-centric image/video synthesis, by clarifying the motivation, the typical methodologies, the prospective trends, and the potential industrial applications.
Time |
Schedule |
---|---|
Location: | Date: Friday, 19 June 2020 from 13:20 pm PDT to 18:30 pm PDT. (All times are Pacific Daylight Time, Seattle time). |
13:20-13:40 | Opening remarks and best paper talk [YouTube Video1] [Bilibili Video1] [YouTube Video2] |
13:40-14:20 | Invited talk 1: Ira Kemelmacher-Shlizerman, Associate Professor, University of Washington [YouTube] [Bilibili] Talk title: Human Modeling and Synthesis |
14:20:15:00 | Invited talk 2: William T. Freeman, Professor, MIT [YouTube] [Bilibili] Talk title: Learning from videos playing forwards, backwards, fast, and slow |
15:00-15:15 | Winner talk 1: Winner of the Multi-Person Human Parsing Challenge [YouTube] [Bilibili] [Slide] |
15:15-15:30 | Winner talk 2: Winner of the Video Multi-Person Human Parsing Challenge [YouTube] [Bilibili] [Slide] |
15:30-16:10 | Invited talk 3: Ming-Hsuan Yang, Professor, University of California at Merced [YouTube] [Bilibili] Talk title: Synthesizing Human Images in 2D and 3D Scenes |
16:10-16:50 | Invited talk 4: Jun-Yan Zhu, Assistant Professor, Carnegie Mellon University [YouTube] [Bilibili] Talk title: Visualizing and Understanding GANs |
16:50-17:05 | Winner talk 3: Winner of the Image-based Multi-pose Virtual Try-on Challenge [YouTube] [Bilibili] [Slide] |
17:05-17:20 | Winner talk 4: Winner of the Video Virtual Try-on Challenge [YouTube] [Bilibili] [Slide] |
17:20-17:35 | Winner talk 5: Winner of the Dark Complexion Portrait Segmentation Challenge [YouTube] [Bilibili] [Slide] |
17:35-18:30 | Oral: Epipolar Transformer for Multi-view Human Pose Estimation. |
17:35-18:30 | Oral: Yoga-82: A New Dataset for Fine-grained Classification of Human Poses. |
17:35-18:30 | Oral: The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation. |
17:35-18:30 | Poster: LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking. |
17:35-18:30 | Poster: Fine grained pointing recognition for natural drone guidance. |
17:35-18:30 | Poster: Reposing Humans by Warping 3D Features. |
Important Dates |
|
Look Into Person: Multi-Person Human Parsing Challenge
Look Into Person: Video Mutil-Person Human Parsing Challenge
Look Into Person: Image-based Multi-pose Virtual Try-on Challenge
Look Into Person: Video Virtual Try-on Challenge
Look Into Person: Dark Complexion Portrait Segmentation Challenge
Please feel free to send any question or comments to:
donghy7 AT mail2.sysu.edu.cn, xdliang328 AT gmail.com