TAMF-VTON is a diffusion-based virtual try‑on system that eliminates the need for segmentation masks, parsing maps, pose representations, or spatial controls during inference. It uses token‑wise sparse Mixture‑of‑Experts adaptation to provide input‑dependent capacity, along with timestep‑weighted frequency supervision and adaptive offline data construction to improve high‑frequency reconstruction and cross‑category training. The method achieves top performance on single‑garment VITON‑HD and DressCode benchmarks, supports multi‑garment transfer, and can generate 1K‑resolution images in under 15 seconds on an RTX 4090.
By Jie Wang, Qian He, Gaofeng He, Xiaogang Jin, Huamin Wang
RAGDiffusion++ advances garment generation by addressing the high‑frequency texture gap that previous retrieval‑augmented models left unresolved. The approach introduces a dual‑image FLUX architecture trained on a large, complex garment dataset, coupled with a new attribute‑aware reward model that guides reinforcement learning to favor realistic high‑frequency patterns. An adversarial‑regularized RL strategy (AR‑GRPO) further prevents artifact exploitation, ensuring the model samples authentic, detailed garment textures.
By Yuhan Li, Xianfeng Tan, Fangao Zeng, Wenxiang Shang, Pipei Huang, Hao Zhou, Zhiyu Jin, Wenjun Zhang, Bingbing Ni
arXiv:2607.11233v2 Announce Type: replace
Abstract: Virtual try-on (VTON) is a bi-conditional image generation problem that requires not only accurate person preservation but also faithful garment de...
By Lu Yang, Xiaonan Hu, Yanan Li, Daqi Liu, Hao Lu, Xiang Bai
arXiv:2607. 09362v1 Announce Type: cross Abstract: Virtual try-on (VTO) has made significant progress in realistically transferring garments onto a target person.
By Seungyong Lee, Hyun Jun Jang, Sangoh Kim, Sungjoon Park
arXiv:2610.15582v1 Announce Type: new
Abstract: We study the problem of inverse garment design, reconstructing simulation-ready 3D outfits with arbitrary layers from a single image, with visual agent...
By Nanxiang Jiang, Yunuo Chen, Zeng Tao, Zizhang Li, Qi Guo, Jiajun Wu, Yin Yang, Chenfanfu Jiang
The paper introduces RealFit, a virtual try‑on framework that addresses texture degradation and structural drift caused by symmetric bidirectional attention in Diffusion Transformers. By employing Unidirectional Information Flow to isolate garment conditions from stochastic noise and Decoupled Timestep Modulation to strengthen the conditional signal, RealFit achieves high‑fidelity garment rendering. The method also enables a time‑invariant condition branch and a conditional KV cache, cutting inference time by about 75%.
By Zishu Qin, Zhiyu Jin, Pipei Huang, Hao Zhou
arXiv:2609.39335v1 Announce Type: new
Abstract: Video virtual try-on has attracted increasing attention due to its broad potential in digital fashion and intelligent e-commerce. However, existing met...
By Zijing Qin, Jun Zhou, Ruicheng Zhang, Jiaqi Hou, Zunnan Xu, Ronghui Li, Zhenyu Xie, Xiu Li
DetailAnywhere introduces a new fashion detail generation task that focuses on generating photorealistic close‑up views of specific garment regions, such as collars or cuffs, based on a focus marker in a reference image. The authors release FDBench, a benchmark of over 40,000 human‑verified detail pairs across 41 categories, and propose Cross‑Modal Feature Alignment Distillation (CFAD) to align multimodal diffusion branches using a DINOv3 teacher. A consistency reward model further improves alignment, and experiments show DetailAnywhere surpasses existing open‑source methods in both quantitative metrics and human evaluation.
By Zijun Li, Yimin Zhou, Jia Sun, Honglie Wang, Pengcheng Wei, Junlong Wu, Yongrui Heng, Jiyuan Wang, Huan Ouyang, Boheng Zhang, Huaiqing Wang, Dewen Fan, Qianqian Gan, Fan Yang, Tingting Gao
arXiv:2608.29804v1 Announce Type: new
Abstract: Virtual try-on (VTON) requires not only realistic generation but also faithful preservation of garment characteristics. However, existing evaluation me...
By Kaidong Zhang, Yukang Ding, Xiaoyu Liu, Ying Chen
GarmentSegAgent is an autonomous framework for garment segmentation in web fashion images that combines semantic planning, spatial execution, and visual evaluation through iterative textual self‑refinement. Its core component, CAST (Constrained Attribution‑guided Selective TextGrad), links visual error attribution to targeted textual optimization, updating instruction components that cause segmentation errors while respecting garment‑specific constraints. Experiments on DeepFashion2, ModaNet, and Fashionpedia show consistent gains in region overlap and boundary accuracy, outperforming conventional TextGrad and enabling reliable inference‑time error correction without freezing foundation models.
By Jia Chen, Chao Huang, Xinrong Hu, Mingdi Huang, Ruyang Zhao, Jun Bai
MMTryon is a multi‑modal, multi‑reference virtual try‑on framework that generates high‑quality compositional try‑on results using text instructions and multiple garment images. It addresses three overlooked problems: supporting multiple try‑on items, allowing dressing style specification via text, and eliminating reliance on segmentation models by using a parsing‑free garment encoder and a scalable data generation pipeline. Experiments on high‑resolution benchmarks and in‑the‑wild test sets show MMTryon outperforms state‑of‑the‑art methods qualitatively and quantitatively.
By Xujie Zhang, Ente Lin, Michael Kampffmeyer, Zhenyu Xie, Jiang Li, Ting Liu, Xiaochao Qu, Luoqi Liu, Xiaodan Liang
arXiv:2608. 05745v1 Announce Type: cross Abstract: Video Virtual Try-On (VVT) synthesizes a video of a person wearing a target garment while preserving identity, motion, and scene dynamics.
By Yushe Cao, Shikun Feng, Fei Shen, Haikuo Peng, Jianqiang Xia, Yiheng Zhu, Dianxi Shi, Chun Yu