most citedSynthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objects

16 citations · 29 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI20238 cited

State of the Art on Diffusion Models for Visual Computing

Ryan Po, Wang Yifan, Vladislav Golyanik +15

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…

cs.CV2023

Object Motion Guided Human Motion Synthesis

Jiaman Li, Jiajun Wu, C. Karen Liu

Modeling human behaviors in contextual environments has a wide range of applications in character animation, embodied AI, VR/AR, and robotics. In real-world scenarios, humans frequ…

cs.GR2023

DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

Yifeng Jiang, Jungdam Won, Yuting Ye +1

Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports…

cs.GR20231 cited

Anatomically Detailed Simulation of Human Torso

Seunghwan Lee, Yifeng Jiang, C. Karen Liu

Existing digital human models approximate the human skeletal system using rigid bodies connected by rotational joints. While the simplification is considered acceptable for legs an…

cs.RO202316 cited

Synthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objects

Sirui Chen, Albert Wu, C. Karen Liu

Daily objects embedded in a contextual environment are often ungraspable initially. Whether it is a book sandwiched by other books on a fully packed bookshelf or a piece of paper l…

cs.CV20233 cited

CIRCLE: Capture In Rich Contextual Environments

Joao Pedro Araujo, Jiaman Li, Karthik Vetrivel +5

Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic activities people perform in the real world. However, conventional optics…