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20242026
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cs.CV2026

Controllable Text-to-Motion Generation via Modular Body-Part Phase Control

Minyue Dai, Ke Fan, Anyi Rao +2

Text-to-motion (T2M) generation is becoming a practical tool for animation and interactive avatars. However, modifying specific body parts while maintaining overall motion coherenc…

cs.CV2025

Towards Synthesized and Editable Motion In-Betweening Through Part-Wise Phase Representation

Minyue Dai, Ke Fan, Bin Ji +5

Styled motion in-betweening is crucial for computer animation and gaming. However, existing methods typically encode motion styles by modeling whole-body motions, often overlooking…

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…

cs.CV2024

ChatDyn: Language-Driven Multi-Actor Dynamics Generation in Street Scenes

Yuxi Wei, Jingbo Wang, Yuwen Du +6

Generating realistic and interactive dynamics of traffic participants according to specific instruction is critical for street scene simulation. However, there is currently a lack…

cs.CV2024

DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters

Mingze Sun, Junhao Chen, Junting Dong +7

Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging tas…

cs.CV2024

RoomTex: Texturing Compositional Indoor Scenes via Iterative Inpainting

Qi Wang, Ruijie Lu, Xudong Xu +5

The advancement of diffusion models has pushed the boundary of text-to-3D object generation. While it is straightforward to composite objects into a scene with reasonable geometry,…