3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models

• URL: http://arxiv.org/abs/2504.17414v1
• Date: Thu, 24 Apr 2025 10:12:40 GMT
• Title: 3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models
• Authors: Min Wei, Chaohui Yu, Jingkai Zhou, Fan Wang,
• Abstract summary: 3DV-TON is a novel framework for generating high-fidelity and temporally consistent video try-on results.<n>Our approach employs generated animatable textured 3D meshes as explicit frame-level guidance.<n>To advance video try-on research, we introduce HR-VVT, a high-resolution benchmark dataset containing 130 videos with diverse clothing types and scenarios.
• Score: 12.949009540192389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video try-on replaces clothing in videos with target garments. Existing methods struggle to generate high-quality and temporally consistent results when handling complex clothing patterns and diverse body poses. We present 3DV-TON, a novel diffusion-based framework for generating high-fidelity and temporally consistent video try-on results. Our approach employs generated animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. This is achieved by enabling direct reference to consistent garment texture movements throughout video sequences. The proposed method features an adaptive pipeline for generating dynamic 3D guidance: (1) selecting a keyframe for initial 2D image try-on, followed by (2) reconstructing and animating a textured 3D mesh synchronized with original video poses. We further introduce a robust rectangular masking strategy that successfully mitigates artifact propagation caused by leaking clothing information during dynamic human and garment movements. To advance video try-on research, we introduce HR-VVT, a high-resolution benchmark dataset containing 130 videos with diverse clothing types and scenarios. Quantitative and qualitative results demonstrate our superior performance over existing methods. The project page is at this link https://2y7c3.github.io/3DV-TON/

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