Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates

• URL: http://arxiv.org/abs/2602.24043v1
• Date: Fri, 27 Feb 2026 14:19:23 GMT
• Title: Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates
• Authors: Yingxuan You, Ren Li, Corentin Dumery, Cong Cao, Hao Li, Pascal Fua,
• Abstract summary: Reconstructing 3D clothed humans from monocular images and videos is a fundamental problem with applications in virtual try-on, avatar creation, and mixed reality.<n>We propose a high-fidelity 3D garment reconstruction from both single images and sequences.<n>The reconstructed garments preserve fine geometric detail while exhibiting realistic dynamic motion, supporting downstream applications such as texture editing, garment Sewing, and animation.
• Score: 38.93906389023275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing 3D clothed humans from monocular images and videos is a fundamental problem with applications in virtual try-on, avatar creation, and mixed reality. Despite significant progress in human body recovery, accurately reconstructing garment geometry, particularly for loose-fitting clothing, remains an open challenge. We propose a unified framework for high-fidelity 3D garment reconstruction from both single images and video sequences. Our approach combines Implicit Sewing Patterns (ISP) with a generative diffusion model to learn expressive garment shape priors in 2D UV space. Leveraging these priors, we introduce a mapping model that establishes correspondences between image pixels, UV pattern coordinates, and 3D geometry, enabling accurate and detailed garment reconstruction from single images. We further extend this formulation to dynamic reconstruction by introducing a spatio-temporal diffusion scheme with test-time guidance to enforce long-range temporal consistency. We also develop analytic projection-based constraints that preserve image-aligned geometry in visible regions while enforcing coherent completion in occluded areas over time. Although trained exclusively on synthetically simulated cloth data, our method generalizes well to real-world imagery and consistently outperforms existing approaches on both tight- and loose-fitting garments. The reconstructed garments preserve fine geometric detail while exhibiting realistic dynamic motion, supporting downstream applications such as texture editing, garment retargeting, and animation.

Related papers

• DressWild: Feed-Forward Pose-Agnostic Garment Sewing Pattern Generation from In-the-Wild Images [50.11081091174558]
  This paper focuses on sewing pattern generation for garment modeling and fabrication applications.<n>We propose DressWild, a novel feed-forward pipeline that reconstructs physics-consistent 2D sewing patterns and the corresponding 3D garments from a single in-the-wild image.
  arXiv  Detail & Related papers  (2026-02-18T14:45:15Z)
• ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction [32.919669263399534]
  ReWeaver is a novel framework for topology-accurate 3D garment and sewing pattern reconstruction from sparse multi-view RGB images.<n>We construct a large-scale dataset GCD-TS, comprising multi-view RGB images, 3D garment geometries, textured human body meshes and annotated sewing patterns.
  arXiv  Detail & Related papers  (2026-01-23T11:42:02Z)
• Single View Garment Reconstruction Using Diffusion Mapping Via Pattern Coordinates [45.48311596587306]
  Reconstructing 3D clothed humans from images is fundamental to applications like virtual try-on, avatar creation, and mixed reality.<n>We present a novel method for high-fidelity 3D garment reconstruction from single images that bridges 2D and 3D representations.
  arXiv  Detail & Related papers  (2025-04-11T08:39:18Z)
• Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics [27.697150953628572]
  This paper focuses on 3D garment generation, a key area for applications like virtual try-on with dynamic garment animations.<n>We introduce Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image.
  arXiv  Detail & Related papers  (2025-02-05T18:49:03Z)
• USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency [41.89803177312638]
  We propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models. Our method proposes a generalized surface-aware neural radiance field to learn the mapping between sparse multi-view images and geometries of the dressed people.
  arXiv  Detail & Related papers  (2023-02-21T08:48:27Z)
• Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion [54.151979979158085]
  We introduce a principled end-to-end reconstruction framework for natural images, where accurate ground-truth poses are not available. We leverage an unconditional 3D-aware generator, to which we apply a hybrid inversion scheme where a model produces a first guess of the solution. Our framework can de-render an image in as few as 10 steps, enabling its use in practical scenarios.
  arXiv  Detail & Related papers  (2022-11-21T17:42:42Z)
• 3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective [96.65476492200648]
  This research aims to study a self-supervised 3D clothing reconstruction method. It recovers the geometry shape, and texture of human clothing from a single 2D image.
  arXiv  Detail & Related papers  (2022-04-27T17:46:55Z)
• ARCH++: Animation-Ready Clothed Human Reconstruction Revisited [82.83445332309238]
  We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the visible regions from input views and the unseen regions.
  arXiv  Detail & Related papers  (2021-08-17T19:27:12Z)
• Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images [5.775625085664381]
  We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. Key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously. We show the system advances the frontier of 3D human body and pose reconstruction from single images by quantitative evaluations and comparisons with state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-22T04:26:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.