FitFlow: Decoupled Flow Matching Model for Fit-aware Virtual Try-On

Virtual try-on (VTON) has evolved from merely generating visually appealing results to focusing on the precise fit between the garment and the target body. Existing methods typically rely on simulated data and leverage numerical control or a structured prompt to achieve garment fit. However, such control signals lack spatial precision for continuous fit adjustment. While the reliance on synthetic data fails to capture fine-grained fit textures (e.g., elasticity and subtle draping), leading to visual realism degradation. Motivated by the nature of flow-matching trajectories, where high-noise timesteps establish global layout and low-noise timesteps restore fine details, we propose FitFlow, a decoupled flow matching model that leverages large-scale synthetic data to learn global body-aware fitting and real try-on pairs at late timesteps to recover fine-grained fit textures. We introduce a simple Garment Size Encoding, that warps the garment image onto a canonical grid based on its relative size to the target body, providing continuous fit control while maintaining photorealistic garment appearance. We also develop a measurement-annotated try-on dataset and evaluation protocol for authentic try-on and fit analysis. Experiments demonstrate that our method achieves improved fit consistency, spatial controllability, and visual fidelity over state-of-the-art baselines.