Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics

• URL: http://arxiv.org/abs/2502.03449v1
• Date: Wed, 05 Feb 2025 18:49:03 GMT
• Title: Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics
• Authors: Xuan Li, Chang Yu, Wenxin Du, Ying Jiang, Tianyi Xie, Yunuo Chen, Yin Yang, Chenfanfu Jiang,
• Abstract summary: This paper focuses on 3D garment generation, a key area for applications like virtual try-on with dynamic garment animations. 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.
• Score: 27.697150953628572
• License:
• Abstract: Recent advances in large models have significantly advanced image-to-3D reconstruction. However, the generated models are often fused into a single piece, limiting their applicability in downstream tasks. This paper focuses on 3D garment generation, a key area for applications like virtual try-on with dynamic garment animations, which require garments to be separable and simulation-ready. 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. Starting with the image, our approach combines a pre-trained image-to-sewing pattern generation model for creating coarse sewing patterns with a pre-trained multi-view diffusion model to produce multi-view images. The sewing pattern is further refined using a differentiable garment simulator based on the generated multi-view images. Versatile experiments demonstrate that our optimization approach substantially enhances the geometric alignment of the reconstructed 3D garments and humans with the input image. Furthermore, by integrating a texture generation module and a human motion generation module, we produce customized physics-plausible and realistic dynamic garment demonstrations. Project page: https://dress-1-to-3.github.io/

Related papers

• IPVTON: Image-based 3D Virtual Try-on with Image Prompt Adapter [64.03091978606952]
  Given a pair of images depicting a person and a garment separately, image-based 3D virtual try-on methods aim to reconstruct a 3D human model. We present IPVTON, a novel image-based 3D virtual try-on framework.
  arXiv  Detail & Related papers  (2025-01-26T17:51:03Z)
• Garment3DGen: 3D Garment Stylization and Texture Generation [11.836357439129301]
  Garment3DGen is a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. We leverage the recent progress of image-to-3D diffusion methods to generate 3D garment geometries. We generate high-fidelity texture maps that are globally and locally consistent and faithfully capture the input guidance.
  arXiv  Detail & Related papers  (2024-03-27T17:59:33Z)
• AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model [58.035758145894846]
  We introduce AniDress, a novel method for generating animatable human avatars in loose clothes using very sparse multi-view videos. A pose-driven deformable neural radiance field conditioned on both body and garment motions is introduced, providing explicit control of both parts. Our method is able to render natural garment dynamics that deviate highly from the body and well to generalize to both unseen views and poses.
  arXiv  Detail & Related papers  (2024-01-27T08:48:18Z)
• En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data [36.51674664590734]
  We present En3D, an enhanced izable scheme for high-qualityd 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalance viewing angles and pose priors, our approach aims to develop a zero-shot 3D capable of producing 3D humans.
  arXiv  Detail & Related papers  (2024-01-02T12:06:31Z)
• Towards Garment Sewing Pattern Reconstruction from a Single Image [76.97825595711444]
  Garment sewing pattern represents the intrinsic rest shape of a garment, and is the core for many applications like fashion design, virtual try-on, and digital avatars. We first synthesize a versatile dataset, named SewFactory, which consists of around 1M images and ground-truth sewing patterns. We then propose a two-level Transformer network called Sewformer, which significantly improves the sewing pattern prediction performance.
  arXiv  Detail & Related papers  (2023-11-07T18:59:51Z)
• Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives [70.32817882783608]
  We present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. Unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points.
  arXiv  Detail & Related papers  (2023-07-11T17:58:31Z)
• PERGAMO: Personalized 3D Garments from Monocular Video [6.8338761008826445]
  PERGAMO is a data-driven approach to learn a deformable model for 3D garments from monocular images. We first introduce a novel method to reconstruct the 3D geometry of garments from a single image, and use it to build a dataset of clothing from monocular videos. We show that our method is capable of producing garment animations that match the real-world behaviour, and generalizes to unseen body motions extracted from motion capture dataset.
  arXiv  Detail & Related papers  (2022-10-26T21:15:54Z)
• Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images [82.32776379815712]
  We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. We adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network. Our model is robust to the quality of the initial mesh and the error of camera pose, and can be combined with a differentiable function for test-time optimization.
  arXiv  Detail & Related papers  (2022-04-21T03:42:31Z)
• SMPLpix: Neural Avatars from 3D Human Models [56.85115800735619]
  We bridge the gap between classic rendering and the latest generative networks operating in pixel space. We train a network that directly converts a sparse set of 3D mesh vertices into photorealistic images. We show the advantage over conventional differentiables both in terms of the level of photorealism and rendering efficiency.
  arXiv  Detail & Related papers  (2020-08-16T10:22:00Z)

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.