3 citations · 4 across the 9 of their papers we have counts for
5 papers · 2 filters
Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
Yue Zhang, Zun Wang, Han Lin +3
Spatial reasoning is a fundamental capability for vision-language models (VLMs) deployed in real-world environments. However, visual observations are inherently limited representat…
PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation
Yidong Huang, Zun Wang, Han Lin +6
Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has driven recent gains in general…
V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
Han Lin, Xichen Pan, Zun Wang +4
Pixel-space diffusion has recently re-emerged as a strong alternative to latent diffusion, enabling high-quality generation without pretrained autoencoders. However, standard pixel…
AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
Zun Wang, Han Lin, Jaehong Yoon +3
Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition gene…
When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
Shoubin Yu, Yue Zhang, Zun Wang +4
Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent…