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20192026
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cs.CV2026

VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24

Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current…

cs.CV2026

4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

Renlong Wu, Haoran Chen, Yuxiang Wei +3

Generating high-quality 360-degree dynamic human assets from text prompts is challenging. Existing methods usually synthesize monocular or multi-view videos first and then fit a 4D…

cs.CV2026

To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations

Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4

Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods typically follow either background…

cs.CV2026

TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs

Yuhan Zhu, Changlian Ma, Xiangyu Zeng +12

Video multimodal large language models (MLLMs) can describe what happens in a video, but rarely identify when the supporting evidence occurs. We study generalist video temporal gro…

cs.CV2026

CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping

Haoyu Zhao, Jiaxi Gu, Haoran Chen +11

Precise camera pose control is critical for video diffusion, yet maintaining geometric consistency remains a challenge. Existing methods that directly inject numerical camera param…

cs.CV2026

CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation

Haoyu Zhao, Zihao Zhang, Jiaxi Gu +10

Camera-controllable video generation aims to synthesize videos with flexible and physically plausible camera movements. However, existing methods either provide imprecise camera co…