6 papers
Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation
Xinshun Wang, Peiming Li, Ziyi Wang +5
Human motion analysis tasks, such as temporal 3D pose estimation, motion prediction, and motion in-betweening, play an essential role in computer vision. However, current paradigms…
Point-In-Context: Understanding Point Cloud via In-Context Learning
Mengyuan Liu, Zhongbin Fang, Xia Li +4
The rise of large-scale models has catalyzed in-context learning as a powerful approach for multitasking, particularly in natural language and image processing. However, its applic…
Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning
Mengyuan Liu, Xinshun Wang, Zhongbin Fang +6
This paper aims to model 3D human motion across domains, where a single model is expected to handle multiple modalities, tasks, and datasets. Existing cross-domain models often rel…
Yan: Foundational Interactive Video Generation
Deheng Ye, Fangyun Zhou, Jiacheng Lv +15
We present Yan, a foundational framework for interactive video generation, covering the entire pipeline from simulation and generation to editing. Specifically, Yan comprises three…
Playable Game Generation
Mingyu Yang, Junyou Li, Zhongbin Fang +5
In recent years, Artificial Intelligence Generated Content (AIGC) has advanced from text-to-image generation to text-to-video and multimodal video synthesis. However, generating pl…
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Xinshun Wang, Zhongbin Fang, Xia Li +2
In-context learning provides a new perspective for multi-task modeling for vision and NLP. Under this setting, the model can perceive tasks from prompts and accomplish them without…