5 papers
Generative Motion In-betweening by Diffusion over Continuous Implicit Representations
Shiyu Fan, Paul Henderson, Edmond S. L. Ho
Recent advances in generative models have yielded impressive progress on motion in-betweening, allowing for more complex, varied, and realistic motion transitions. However, recent…
Masked Generative Policy for Robotic Control
Lipeng Zhuang, Shiyu Fan, Florent P. Audonnet +4
We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer…
Waymo-3DSkelMo: A Multi-Agent 3D Skeletal Motion Dataset for Pedestrian Interaction Modeling in Autonomous Driving
Guangxun Zhu, Shiyu Fan, Hang Dai +1
Large-scale high-quality 3D motion datasets with multi-person interactions are crucial for data-driven models in autonomous driving to achieve fine-grained pedestrian interaction u…
Multi-Person Interaction Generation from Two-Person Motion Priors
Wenning Xu, Shiyu Fan, Paul Henderson +1
Generating realistic human motion with high-level controls is a crucial task for social understanding, robotics, and animation. With high-quality MOCAP data becoming more available…
Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation
Lipeng Zhuang, Shiyu Fan, Yingdong Ru +3
We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations…