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20242026
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cs.CV2026

GIDE: Unlocking Diffusion LLMs for Precise Training-Free Image Editing

Zifeng Zhu, Jiaming Han, Jiaxiang Zhao +2

While Diffusion Large Language Models (DLLMs) have demonstrated remarkable capabilities in multi-modal generation, performing precise, training-free image editing remains an open c…

cs.CV2026

Cubic Discrete Diffusion: Discrete Visual Generation on High-Dimensional Representation Tokens

Yuqing Wang, Chuofan Ma, Zhijie Lin +7

Visual generation with discrete tokens has gained significant attention as it enables a unified token prediction paradigm shared with language models, promising seamless multimodal…

cs.CV2026

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

Yuang Ai, Jiaming Han, Shaobin Zhuang +8

We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets…

cs.CV2026

UniWeTok: An Unified Binary Tokenizer with Codebook Size for Unified Multimodal Large Language Model

Shaobin Zhuang, Yuang Ai, Jiaming Han +12

Unified Multimodal Large Language Models (MLLMs) require a visual representation that simultaneously supports high-fidelity reconstruction, complex semantic extraction, and generat…

cs.CV2025

CrossLMM: Decoupling Long Video Sequences from LMMs via Dual Cross-Attention Mechanisms

Shilin Yan, Jiaming Han, Joey Tsai +5

The advent of Large Multimodal Models (LMMs) has significantly enhanced Large Language Models (LLMs) to process and interpret diverse data modalities (e.g., image and video). Howev…

cs.CV2025

ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection

Ruize Ma, Minghong Cai, Yilei Jiang +7

Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer enhanced…