We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typically regress human motion directly from video and train the network with geometric supervision. However, recovering human motion from monocular video is inherently ambigu...
Zhan-Ke Wang, Cheng-Feng Zhao, Qing Shuai et al.· 0 citations
AnimateCanvas is proposed, a model that supports cue-conditioned implicit motion planning to faithfully and coherently connect all cues, dense or sparse, full or partial, into one full-body motion sequence, and achieves state-of-the-art results in temporal completion, spatial control, sequential generation, language-gu...
Ze-Yu Ling, Di Kang, Qing Shuai et al.· 0 citations
Professional character animation requires both natural motion and precise, versatile control. For example, it is common for the creators to define the timing of a specified action, to control the motion range of the character's arm swing, and the route the character walks through, like specifying various kinematic moti...
Ze-Yu Ling, Di Kang, Qing Shuai et al.· 0 citations
VGI-bench is introduced, containing 27 tasks and 810 instances, organized by a two-level taxonomy of task domains and skill tags for fine-grained evaluation of visual reasoning capabilities of video generation models, and it is hoped VGI-bench will help stimulate the development of next-generation video generation mode...
Xuan He, Cong Wei, Yuhao Cheng et al.· 2 citations
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