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VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
Junxiang Xu, Ruisi Wang, Fanyi Pu +49
Native visual reasoning treats visual generation as the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be understood or outputs to be r…
Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer
Chenyang Gu, Mingyuan Zhang, Haozhe Xie +3
Prior motion generation largely follows two paradigms: continuous diffusion models that excel at kinematic control, and discrete token-based generators that are effective for seman…
Demystifying Video Reasoning
Ruisi Wang, Zhongang Cai, Fanyi Pu +11
Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabilities. Prior work attributes this to a…
Scaling Spatial Intelligence with Multimodal Foundation Models
Zhongang Cai, Ruisi Wang, Chenyang Gu +26
Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation m…
Holistic Evaluation of Multimodal LLMs on Spatial Intelligence
Zhongang Cai, Yubo Wang, Qingping Sun +21
Multimodal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, the very cap…
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation
Yukang Cao, Xinying Guo, Mingyuan Zhang +3
While recent advances in text-to-motion generation have shown promising results, they typically assume all individuals are grouped as a single unit. Scaling these methods to handle…
Large Motion Model for Unified Multi-Modal Motion Generation
Mingyuan Zhang, Daisheng Jin, Chenyang Gu +8
Human motion generation, a cornerstone technique in animation and video production, has widespread applications in various tasks like text-to-motion and music-to-dance. Previous wo…