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20242026
most citedAdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing

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cs.CV2026

Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models

Junhao Liu, Jian-Wei Zhang, Tao Huang +3

Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial inst…

cs.CV2026

Rosetta: Composable Native Multimodal Pretraining

Xiangyue Liu, Zijian Zhang, Miles Yang +3

Achieving true artificial general intelligence requires foundation models capable of integrating new modalities without forgetting prior knowledge. However, accommodating continuou…

cs.CV2026

TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing

Peizhen Zhang, Yang Li, Xunsong Li +10

Modern image generation model rapidly grows their sizes to meet high-fidelity image synthesis. However, they gradually become unaffordable for their enormous parameter consumption…

cs.CV2026

CrossFlow: One-Step Generation Across Latent and Pixel Spaces

Xiyuan Wang, Xiao Zhang, Yang Li +4

Most diffusion and flow-matching generators define the prior, probability path, and prediction target in the same representation space. Latent diffusion improves efficiency by movi…

cs.CV2026

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers

Guozhen Zhang, Xuerui Qiu, Yutao Cui +11

Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space. In this paper, we present HYDR…

cs.CV2026

Baton: Explicit Semantic Blueprints for Joint Video-Audio Generation

Shuyuan Tu, Qi Tian, Zihan Yang +9

Current open-source diffusion models struggle to generate stable and synchronized audio-visual content, particularly in scenarios demanding complex semantic reasoning. The root cau…