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:2608.29804v1 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
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:2601. 22725v4 Announce Type: replace-cross Abstract: Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remains a persistent bottleneck.
By Jin Li, Tao Chen, Kai Wen, Siqi Yin, Shuai Jiang, Weijie Wang, Jingwen Luo, Chenhui Wu
arXiv:2610.36506v1 Announce Type: new
Abstract: Web fashion images support applications such as visual product search and consumer-to-shop matching, yet their visual heterogeneity makes reliable garm...
By Jia Chen, Chao Huang, Xinrong Hu, Mingdi Huang, Ruyang Zhao, Jun Bai
arXiv:2609.14100v1 Announce Type: cross
Abstract: Fashion Image Captioning (FIC) plays a vital role in enhancing user experience and product search in e-commerce platforms. Unlike natural scene image...
By Abhirama Subramanyam Penamakuri, Shreya Shukla, Anand Mishra
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
Web fashion images support applications such as visual product search and consumer-to-shop matching, yet their visual heterogeneity makes reliable garment segmentation challenging. Despite advances in...
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
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
arXiv:2610.19623v1 Announce Type: new
Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layere...
By Jia Chen, Ruyang Zhao, Jun Bai, Chao Huang, Saishang Zhong, Xinrong Hu