3 papers
cs.CV2025
Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing
Ling-Hao Chen, Shunlin Lu, Wenxun Dai +5
This research delves into the problem of interactive editing of human motion generation. Previous motion diffusion models lack explicit modeling of the word-level text-motion corre…
cs.CV2025
Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset
Yuhong Zhang, Jing Lin, Ailing Zeng +7
In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lack…
cs.CV2024
ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model
Shunlin Lu, Jingbo Wang, Zeyu Lu +6
The scaling law has been validated in various domains, such as natural language processing (NLP) and massive computer vision tasks; however, its application to motion generation re…