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20232026
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cs.CV2026

LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing

Jianzong Wu, Hao Lian, Jiongfan Yang +12

Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier field. Existing unified framewo…

cs.CV2026

Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation

Junjie Wang, Xinghua Lou, Jason Li +8

Text-to-Image (T2I) models and Unified Multimodal Models (UMMs) have achieved remarkable progress in visual generation. However, their reliance on a single-pass generation paradigm…

cs.CV2026

SAMTok: Representing Any Mask with Two Words

Yikang Zhou, Tao Zhang, Dengxian Gong +13

Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-lev…

cs.CV2025

MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation

Ye Tian, Ling Yang, Jiongfan Yang +10

While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxic…

cs.CV2025

Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs

Haochen Wang, Yuhao Wang, Tao Zhang +13

While Multimodal Large Language Models (MLLMs) excel at holistic understanding, they struggle in capturing the dense world with complex scenes, requiring fine-grained analysis of i…

cs.CV2024

DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes

Zhaowei Wang, Hongming Zhang, Tianqing Fang +6

Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. However, their potential to comprehend e…