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

• URL: http://arxiv.org/abs/2603.08147v1
• Date: Mon, 09 Mar 2026 09:28:15 GMT
• Title: MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data
• Abstract summary: MV-Fashion is a large-scale, multi-view video dataset engineered for domain-specific fashion analysis.<n>It features 3,273 sequences from 80 diverse subjects wearing 3-10 outfits each.<n>A core contribution is a rich data representation that includes pixel-level semantic annotations.
• Score: 33.49074848598509
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 .

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