3 papers
cs.CV2026
SegviGen: Repurposing 3D Generative Model for Part Segmentation
Lin Li, Haoran Feng, Zehuan Huang +8
We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or…
cs.CV2026
TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation
Yabiao Wang, Shuo Wang, Jiangning Zhang +4
Human-human motion generation is essential for understanding humans as social beings. Current methods fall into two main categories: single-person-based methods and separate modeli…
cs.CV2024
Textual Decomposition Then Sub-motion-space Scattering for Open-Vocabulary Motion Generation
Ke Fan, Jiangning Zhang, Ran Yi +6
Text-to-motion generation is a crucial task in computer vision, which generates the target 3D motion by the given text. The existing annotated datasets are limited in scale, result…