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
Large language models (LLMs) provide limited support for sign language interaction. Unifying sign language translation (SLT) and generation (SLG) to enable sign language as both input and output can reduce switching between separate models during sign-text interaction. We present SignGPT, a unified, pose-based framewor...
Rong-Hui Li, Jun Dong, Zhongyuan Hu 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
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