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
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...
arXiv:2608.23302v1 Announce Type: new
Abstract: Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural mul...
By Matteo Attimonelli, Claudio Pomo, Alessandro De Bellis, Danilo Danese, Dietmar Jannach, Tommaso Di Noia
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
Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem where models must inter...
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