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20242026
most citedAGSwap: Overcoming Category Boundaries in Object Fusion via Adaptive Group Swapping

2 citations · 2 across the 8 of their papers we have counts for

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cs.CV2026

OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation

Zhaotong Yang, Ying Tai, Jiahui Zhan +3

Unified fashion generation integrates tasks like virtual try-on and garment reconstruction into a single model to reduce task-specific adaptation costs. However, naive parameter sh…

cs.CV2025

RMLer: Synthesizing Novel Objects across Diverse Categories via Reinforcement Mixing Learning

Jun Li, Zikun Chen, Haibo Chen +2

Novel object synthesis by integrating distinct textual concepts from diverse categories remains a significant challenge in Text-to-Image (T2I) generation. Existing methods often su…

cs.CV2025

VMDiff: Visual Mixing Diffusion for Limitless Cross-Object Synthesis

Zeren Xiong, Yue Yu, Zedong Zhang +3

Creating novel images by fusing visual cues from multiple sources is a fundamental yet underexplored problem in image-to-image generation, with broad applications in artistic creat…

cs.CV20252 cited

AGSwap: Overcoming Category Boundaries in Object Fusion via Adaptive Group Swapping

Zedong Zhang, Ying Tai, Jianjun Qian +2

Fusing cross-category objects to a single coherent object has gained increasing attention in text-to-image (T2I) generation due to its broad applications in virtual reality, digita…

cs.CV2025

WeatherCycle: Unpaired Multi-Weather Restoration via Color Space Decoupled Cycle Learning

Wenxuan Fang, Jiangwei Weng, Jianjun Qian +2

Unsupervised image restoration under multi-weather conditions remains a fundamental yet underexplored challenge. While existing methods often rely on task-specific physical priors,…

cs.CV2025

When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration

Wenxuan Fang, Jili Fan, Chao Wang +5

Adverse Weather Image Restoration (AWIR) is a highly challenging task due to the unpredictable and dynamic nature of weather-related degradations. Traditional task-specific methods…