Author

Yuqun Wu

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Preprint Oct 2026

Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs

Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial reasoning. However, we find that simply fusing geometry features and training on standard spatial QA yields only marginal improvements on high-level multi-hop tasks. We at...

Yuqun Wu, Yao Xiao, Chu-Hang Zou et al. · 0 citations

Preprint Oct 2026

4Director: Controlling Video World Models with Rigid 3D Geometry

Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through image-plane cues that are ambiguous in depth and rotation or through 3D tracks and blobs that lack complete geometry and lose consistency across viewpoint changes. We int...

Wei Cao, Hao Zhang, Vikram Voleti et al. · 1 citation

#machine learning Preprint Sep 2026

SAM-V: Geometry-Aware Segment Anything for Multi-View Instance Segmentation

SAM-V introduces a prompt-fusion mechanism that enriches sparse SAM prompt tokens with view-specific camera tokens and local VGGT features, making the prompt representation both view-aware and spatially grounded, together with a mask decoder that attends to dense 2D and 3D features.

Jiang-Shan Gong, Yuqun Wu, Qiqian Fu et al. · 1 citation

#machine learning Jul 2026

ReToken: One Token to Improve Vision-Language Models for Visual Retrieval

ReToken is a single learnable embedding trained as an explicit retrieval target that selects a sparse set of query-relevant visual tokens from a pre-filled visual KV cache that yields consistent gains across image and video benchmarks.

Yao Xiao, Reuben Tan, Zhen Zhu et al. · arXiv.org · 0 citations