MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data

TL;DR

MV-Fashion is a comprehensive multi-view video dataset designed to advance fashion analysis tasks like virtual try-on and size estimation by providing realistic garment dynamics, detailed annotations, and paired data.

Contribution

The paper introduces MV-Fashion, a large-scale dataset with multi-view videos, detailed annotations, and paired images, specifically addressing limitations of existing datasets for fashion research.

Findings

Established baseline results for virtual try-on and size estimation.

Demonstrated the dataset's effectiveness in capturing complex garment dynamics.

Provided a new resource for advancing fashion-centric computer vision tasks.

Abstract

Existing 4D human datasets fall short for fashion-specific research, lacking either realistic garment dynamics or task-specific annotations. Synthetic datasets suffer from a realism gap, whereas real-world captures lack the detailed annotations and paired data required for virtual try-on (VTON) and size estimation tasks. To bridge this gap, we introduce MV-Fashion, a large-scale, multi-view video dataset engineered for domain-specific fashion analysis. MV-Fashion features 3,273 sequences (72.5 million frames) from 80 diverse subjects wearing 3-10 outfits each. It is designed to capture complex, real-world garment dynamics, including multiple layers and varied styling (e.g. rolled sleeves, tucked shirt). A core contribution is a rich data representation that includes pixel-level semantic annotations, ground-truth material properties like elasticity, and 3D point clouds. Crucially for VTON applications, MV-Fashion provides paired data: multi-view synchronized captures of worn garments alongside their corresponding flat, catalogue images. We leverage this dataset to establish baselines for fashion-centric tasks, including virtual try-on, clothing size estimation, and novel view synthesis. The dataset is available at https://hunorlaczko.github.io/MV-Fashion .