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cs.CV2026

Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm

Yaofang Liu, Kangning Cui, Meng Chu +7

Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generators still ask users to seria…

cs.CV2026

AEGIS: Exploring the Limit of World Knowledge Capabilities for Unified Mulitmodal Models

Jintao Lin, Bowen Dong, Weikang Shi +4

The capability of Unified Multimodal Models (UMMs) to apply world knowledge across diverse tasks remains a critical, unresolved challenge. Existing benchmarks fall short, offering…

cs.CV2025

MMMamba: A Versatile Cross-Modal In Context Fusion Framework for Pan-Sharpening and Zero-Shot Image Enhancement

Yingying Wang, Xuanhua He, Chen Wu +5

Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by integrating a high-resolution panchromatic (PAN) image with its corresponding low-resolution multispe…

cs.CV2025

VisionDirector: Vision-Language Guided Closed-Loop Refinement for Generative Image Synthesis

Meng Chu, Senqiao Yang, Haoxuan Che +8

Generative models can now produce photorealistic imagery, yet they still struggle with the long, multi-goal prompts that professional designers issue. To expose this gap and better…

cs.CV2025

Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation

Yaofang Liu, Yumeng Ren, Aitor Artola +9

The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed…

cs.CV2025

Disentangling Instruction Influence in Diffusion Transformers for Parallel Multi-Instruction-Guided Image Editing

Hui Liu, Bin Zou, Suiyun Zhang +3

Instruction-guided image editing enables users to specify modifications using natural language, offering more flexibility and control. Among existing frameworks, Diffusion Transfor…