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cs.CV2026

Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation

Weimin Bai, Yubo Li, Weijian Luo +4

Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations…

cs.CV2026

LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)

Wei Luo, Yiting Lu, Xin Li +32

This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and 4D generation settings. The…

cs.CV2026

Multimodal OCR: Parse Anything from Documents

Handong Zheng, Yumeng Li, Kaile Zhang +22

We present Multimodal OCR (MOCR), a document parsing paradigm that jointly parses text and graphics into unified textual representations. Unlike conventional OCR systems that focus…

cs.CV2026

ZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learning

Zihan Ye, Shreyank N Gowda, Kaile Du +2

Zero-shot Learning (ZSL) enables classifiers to recognize classes unseen during training, commonly via generative two stage methods: (1) learn visual semantic correlations from see…

cs.CV2025

When Images Speak Louder: Mitigating Language Bias-induced Hallucinations in VLMs through Cross-Modal Guidance

Jinjin Cao, Zhiyang Chen, Zijun Wang +3

Vision-Language Models (VLMs) have shown solid ability for multimodal understanding of both visual and language contexts. However, existing VLMs often face severe challenges of hal…

cs.CV2025

Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation

Weimin Bai, Yubo Li, Weijian Luo +2

Score Distillation Sampling (SDS) enables high-quality text-to-3D generation by supervising 3D models through the denoising of multi-view 2D renderings, using a pretrained text-to-…