title: 3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models
summary: 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/
paper_authors._id: 680b3f12c131c3be24f80ce0
paper_authors.name: Min Wei
paper_authors._id: 680b3f12c131c3be24f80ce1
paper_authors.name: Chaohui Yu
paper_authors._id: 680b3f12c131c3be24f80ce2
paper_authors.name: Jingkai Zhou
paper_authors._id: 680b3f12c131c3be24f80ce3
paper_authors.name: Fan Wang
paper_title: 3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models
paper_summary: 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/
paper_ai_summary: A diffusion-based framework generates high-fidelity and temporally consistent video try-on by using animatable textured 3D meshes as frame-level guidance.
paper_ai_keywords: diffusion-based framework 3D meshes frame-level guidance texture movements keyframe HR-VVT