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20242026
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cs.CV2026

RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space

Xichen Pan, Aashu Singh, Satya Narayan Shukla +3

Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically limited to text encoding, while denoising is handled by newly trained generative…

cs.CV2026

Cambrian-P: Pose-Grounded Video Understanding

Jihan Yang, Zifan Zhao, Xichen Pan +6

Camera pose matters. The position and orientation of each viewpoint define a shared spatial coordinate frame that relates observations across video frames. Yet this signal is large…

cs.CV2026

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…

cs.CV2025

Exploring MLLM-Diffusion Information Transfer with MetaCanvas

Han Lin, Xichen Pan, Ziqi Huang +10

Multimodal learning has rapidly advanced visual understanding, largely via multimodal large language models (MLLMs) that use powerful LLMs as cognitive cores. In visual generation,…

cs.CV2025

BLIP3o-NEXT: Next Frontier of Native Image Generation

Jiuhai Chen, Le Xue, Zhiyang Xu +12

We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3 series that advances the next frontier of native image generation. BLIP3o-NEXT unifies text-to-image gener…

cs.CV2025

Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation

Zhiyang Xu, Jiuhai Chen, Zhaojiang Lin +10

Recent advances in large language models (LLMs) have enabled multimodal foundation models to tackle both image understanding and generation within a unified framework. Despite thes…