arXiv Computer Vision By Shuyang Xu, Zhiyang Dou, Yiduo Hao, Zekun Li, Liang Pan, Jingbo Wang, Cheng Lin, Yuan Liu, Wenping Wang, Mingmin Zhao, Taku Komura

MoSAT: Human Motion Generation from Spatial Audio and Textual Description

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arXiv Machine Learning
Aug 19

From Diffusion to Flow: Efficient Motion Generation in MotionGPT3

The paper compares diffusion and rectified flow objectives within the MotionGPT3 framework for text-driven motion generation. Experiments on HumanML3D show that rectified flow converges faster, achieves strong test performance earlier, and matches or exceeds diffusion quality while requiring fewer sampling steps. The study isolates the generative objective’s impact, demonstrating that rectified flow’s benefits transfer to continuous-latent motion generation.

By Jaymin Bhan, JiHong Jeon, SangYeop Jeong
Hugging Face Trending Papers
Aug 6

Vorch-Omni: Multi-Task Orchestration of Sight and Sound

Recent advances in generative video modeling have enabled diverse generation, reference-based synthesis, extension, and editing, but existing approaches often rely on fragmented task-specific models. A general model must distinguish heterogeneous target, source, and reference signals to determine what to generate, preserve, or use as guidance, while reducing interference among tasks.

arXiv Computer Vision
Aug 26

SeMoCo: A Semantic-First Motion Codec for Motion Language Modeling

arXiv:2608.24334v1 Announce Type: new Abstract: Discrete motion representations have substantially advanced autoregressive text-to-motion generation. However, most motion tokenizers are optimized for...

By Tianlv Huang, Hetian Guo, Ziyi Cai, Song Wang, Yanping Zhang, Zipei Fan, Xuan Song, Guangming Wu, Xin Zheng