arXiv Computer Vision
JewelTry: Mask-Free Scale Aware Jewelry Virtual Try-On
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arXiv Computer Vision
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MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation
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.
arXiv Computer Vision
Sep 1
Beyond Global Realism: Virtual Try-On Evaluation and Optimization with Dimension-wise Garment Fidelity Assessment
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...
arXiv Computer Vision
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DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation
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.
arXiv AI
Jun 11
OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation
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.
arXiv Computer Vision
2d ago
GarmentSegAgent: Autonomous Garment Segmentation for Web Fashion Images via Textual Self-Refinement
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...
arXiv AI
Sep 15
RA-CoA: Training-free Fashion Image Captioning via Retrieval-Augmented Chain-of-Attributes
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...