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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…
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…
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…