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
This work proposes Flow3D-OPD, a two-stage post-training framework that introduces multi-teacher distillation into 3D geometry generation and achieves consistent improvements across all geometric quality dimensions and surpasses all teacher models in the average metric.
Zhi-Wei Ning, Zhen-Wu-Yong Zhou, Pu-Hua Jiang et al.· 1 citation
ChunkVAE, a sparse grid variational autoencoder organized around local chunks rather than a global latent volume, is introduced, indicating that local compression can scale geometry while retaining the global interface required downstream.
Kaiyi Zhang, Zhihao Liang, Haolin Liu et al.· 0 citations
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