DeClotH: Decomposable 3D Cloth and Human Body Reconstruction from a Single Image

• URL: http://arxiv.org/abs/2503.19373v1
• Date: Tue, 25 Mar 2025 06:00:15 GMT
• Title: DeClotH: Decomposable 3D Cloth and Human Body Reconstruction from a Single Image
• Authors: Hyeongjin Nam, Donghwan Kim, Jeongtaek Oh, Kyoung Mu Lee,
• Abstract summary: Most existing methods of 3D clothed human reconstruction from a single image treat the clothed human as a single object without distinguishing between cloth and human body.<n>We present DeClotH, which separately reconstructs 3D cloth and human body from a single image.
• Score: 49.69224401751216
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most existing methods of 3D clothed human reconstruction from a single image treat the clothed human as a single object without distinguishing between cloth and human body. In this regard, we present DeClotH, which separately reconstructs 3D cloth and human body from a single image. This task remains largely unexplored due to the extreme occlusion between cloth and the human body, making it challenging to infer accurate geometries and textures. Moreover, while recent 3D human reconstruction methods have achieved impressive results using text-to-image diffusion models, directly applying such an approach to this problem often leads to incorrect guidance, particularly in reconstructing 3D cloth. To address these challenges, we propose two core designs in our framework. First, to alleviate the occlusion issue, we leverage 3D template models of cloth and human body as regularizations, which provide strong geometric priors to prevent erroneous reconstruction by the occlusion. Second, we introduce a cloth diffusion model specifically designed to provide contextual information about cloth appearance, thereby enhancing the reconstruction of 3D cloth. Qualitative and quantitative experiments demonstrate that our proposed approach is highly effective in reconstructing both 3D cloth and the human body. More qualitative results are provided at https://hygenie1228.github.io/DeClotH/.

Related papers

• GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data [61.05815629606135]
  Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model.<n>GeneMAN builds upon a comprehensive collection of high-quality human data.<n>GeneMAN could generate high-quality 3D human models from a single image input, outperforming prior state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-27T18:59:54Z)
• Ultraman: Single Image 3D Human Reconstruction with Ultra Speed and Detail [11.604919466757003]
  We propose a new method called emphUltraman for fast reconstruction of textured 3D human models from a single image. emphUltraman greatly improves the reconstruction speed and accuracy while preserving high-quality texture details.
  arXiv  Detail & Related papers  (2024-03-18T17:57:30Z)
• SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion [35.73448283467723]
  SiTH is a novel pipeline that integrates an image-conditioned diffusion model into a 3D mesh reconstruction workflow. We employ a powerful generative diffusion model to hallucinate unseen back-view appearance based on the input images. For the latter, we leverage skinned body meshes as guidance to recover full-body texture meshes from the input and back-view images.
  arXiv  Detail & Related papers  (2023-11-27T14:22:07Z)
• Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing [54.29207348918216]
  Cloth2Body needs to address new and emerging challenges raised by the partial observation of the input and the high diversity of the output. We propose an end-to-end framework that can accurately estimate 3D body mesh parameterized by pose and shape from a 2D clothing image. As shown by experimental results, the proposed framework achieves state-of-the-art performance and can effectively recover natural and diverse 3D body meshes from 2D images.
  arXiv  Detail & Related papers  (2023-09-28T06:18:38Z)
• TeCH: Text-guided Reconstruction of Lifelike Clothed Humans [35.68114652041377]
  Existing methods often generate overly smooth back-side surfaces with a blurry texture. Motivated by the power of foundation models, TeCH reconstructs the 3D human by leveraging descriptive text prompts. We propose a hybrid 3D representation based on DMTet, which consists of an explicit body shape grid and an implicit distance field.
  arXiv  Detail & Related papers  (2023-08-16T17:59:13Z)
• Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models [9.479195068754507]
  We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety.
  arXiv  Detail & Related papers  (2023-05-19T17:59:18Z)
• DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models [55.71306021041785]
  We present DreamAvatar, a text-and-shape guided framework for generating high-quality 3D human avatars. We leverage the SMPL model to provide shape and pose guidance for the generation. We also jointly optimize the losses computed from the full body and from the zoomed-in 3D head to alleviate the common multi-face ''Janus'' problem.
  arXiv  Detail & Related papers  (2023-04-03T12:11:51Z)
• Detailed Avatar Recovery from Single Image [50.82102098057822]
  This paper presents a novel framework to recover emphdetailed avatar from a single image. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation framework. Our method can restore detailed human body shapes with complete textures beyond skinned models.
  arXiv  Detail & Related papers  (2021-08-06T03:51:26Z)
• Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People [36.30755368202957]
  We present a novel method to improve the accuracy of the 3D reconstruction of clothed human shape from a single image. The accuracy and completeness for reconstruction of clothed people is limited due to the large variation in shape resulting from clothing, hair, body size, pose and camera viewpoint.
  arXiv  Detail & Related papers  (2020-09-29T17:18:00Z)
• Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images [50.34202789543989]
  Deep Fashion3D is the largest collection to date of 3D garment models. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. A novel adaptable template is proposed to enable the learning of all types of clothing in a single network.
  arXiv  Detail & Related papers  (2020-03-28T09:20:04Z)

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.