4 citations · 8 across the 3 of their papers we have counts for
4 papers
DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation
Han Yang, Yian Wang, Yunlong Song +2
Glove-based motion capture is emerging as a scalable approach to collecting dexterous-hand demonstration data. However, due to the kinematic gap between the human and robot hand, t…
TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
Jielin Wu, Shenzhe Yao, Guanqi He +6
Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through reference tracking. However, to us…
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On
Chongjian Ge, Yibing Song, Yuying Ge +3
Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existin…
Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingPreserving Image Content
Han Yang, Ruimao Zhang, Xiaobao Guo +3
Image visual try-on aims at transferring a target clothing image onto a reference person, and has become a hot topic in recent years. Prior arts usually focus on preserving the cha…