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cs.CV2024

A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions

Youliang Zhang, Ronghui Li, Yachao Zhang +4

Extracting physically plausible 3D human motion from videos is a critical task. Although existing simulation-based motion imitation methods can enhance the physical quality of dail…

cs.CV2024

InterDance:Reactive 3D Dance Generation with Realistic Duet Interactions

Ronghui Li, Youliang Zhang, Yachao Zhang +6

Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing w…

cs.CV2024

ManiVideo: Generating Hand-Object Manipulation Video with Dexterous and Generalizable Grasping

Youxin Pang, Ruizhi Shao, Jiajun Zhang +5

In this paper, we introduce ManiVideo, a novel method for generating consistent and temporally coherent bimanual hand-object manipulation videos from given motion sequences of hand…

cs.CV2024

Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns

Ronghui Li, Hongwen Zhang, Yachao Zhang +6

We propose Lodge++, a choreography framework to generate high-quality, ultra-long, and vivid dances given the music and desired genre. To handle the challenges in computational eff…

cs.CV2024

ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusion

Jiajun Zhang, Yuxiang Zhang, Liang An +4

Dynamic and dexterous manipulation of objects presents a complex challenge, requiring the synchronization of hand motions with the trajectories of objects to achieve seamless and p…

cs.CV2024

Layered 3D Human Generation via Semantic-Aware Diffusion Model

Yi Wang, Jian Ma, Ruizhi Shao +4

The generation of 3D clothed humans has attracted increasing attention in recent years. However, existing work cannot generate layered high-quality 3D humans with consistent body s…