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20242026
most citedHolistic Evaluation of Multimodal LLMs on Spatial Intelligence

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

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…

cs.CL2026

MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models

Chenyang Gu, Jiahao Cheng, Meicong Zhang +3

Scientific ideation aims to propose novel solutions within a given scientific context. Existing LLM-based agentic approaches emulate human research workflows, yet inadequately mode…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025★ 1 cited

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…

cs.CV2024

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…

cs.CV2024

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…