GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data

• URL: http://arxiv.org/abs/2411.18624v1
• Date: Wed, 27 Nov 2024 18:59:54 GMT
• Title: GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data
• Authors: Wentao Wang, Hang Ye, Fangzhou Hong, Xue Yang, Jianfu Zhang, Yizhou Wang, Ziwei Liu, Liang Pan,
• Abstract summary: Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. GeneMAN builds upon a comprehensive collection of high-quality human data. GeneMAN could generate high-quality 3D human models from a single image input, outperforming prior state-of-the-art methods.
• Score: 61.05815629606135
• License:
• Abstract: Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. Existing methods face difficulties including a) the varying body proportions captured by in-the-wild human images; b) diverse personal belongings within the shot; and c) ambiguities in human postures and inconsistency in human textures. In addition, the scarcity of high-quality human data intensifies the challenge. To address these problems, we propose a Generalizable image-to-3D huMAN reconstruction framework, dubbed GeneMAN, building upon a comprehensive multi-source collection of high-quality human data, including 3D scans, multi-view videos, single photos, and our generated synthetic human data. GeneMAN encompasses three key modules. 1) Without relying on parametric human models (e.g., SMPL), GeneMAN first trains a human-specific text-to-image diffusion model and a view-conditioned diffusion model, serving as GeneMAN 2D human prior and 3D human prior for reconstruction, respectively. 2) With the help of the pretrained human prior models, the Geometry Initialization-&-Sculpting pipeline is leveraged to recover high-quality 3D human geometry given a single image. 3) To achieve high-fidelity 3D human textures, GeneMAN employs the Multi-Space Texture Refinement pipeline, consecutively refining textures in the latent and the pixel spaces. Extensive experimental results demonstrate that GeneMAN could generate high-quality 3D human models from a single image input, outperforming prior state-of-the-art methods. Notably, GeneMAN could reveal much better generalizability in dealing with in-the-wild images, often yielding high-quality 3D human models in natural poses with common items, regardless of the body proportions in the input images.

Related papers

• HumanGif: Single-View Human Diffusion with Generative Prior [25.516544735593087]
  Motivated by the success of 2D character animation, we propose strong>HumanGif/strong>, a single-view human diffusion model with generative priors. We formulate the single-view-based 3D human novel view and pose synthesis as a single-view-conditioned human diffusion process.
  arXiv  Detail & Related papers  (2025-02-17T17:55:27Z)
• Single Image, Any Face: Generalisable 3D Face Generation [59.9369171926757]
  We propose a novel model, Gen3D-Face, which generates 3D human faces with unconstrained single image input. To the best of our knowledge, this is the first attempt and benchmark for creating photorealistic 3D human face avatars from single images.
  arXiv  Detail & Related papers  (2024-09-25T14:56:37Z)
• PSHuman: Photorealistic Single-view Human Reconstruction using Cross-Scale Diffusion [43.850899288337025]
  PSHuman is a novel framework that explicitly reconstructs human meshes utilizing priors from the multiview diffusion model. It is found that directly applying multiview diffusion on single-view human images leads to severe geometric distortions. To enhance cross-view body shape consistency of varied human poses, we condition the generative model on parametric models like SMPL-X.
  arXiv  Detail & Related papers  (2024-09-16T10:13:06Z)
• HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors [47.62426718293504]
  HumanSplat predicts the 3D Gaussian Splatting properties of any human from a single input image. HumanSplat surpasses existing state-of-the-art methods in achieving photorealistic novel-view synthesis.
  arXiv  Detail & Related papers  (2024-06-18T10:05:33Z)
• HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion [114.15397904945185]
  We propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network. Our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios.
  arXiv  Detail & Related papers  (2023-10-12T17:59:34Z)
• HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation [41.82589219009301]
  We propose HumanNorm, a novel approach for high-quality and realistic 3D human generation. The main idea is to enhance the model's 2D perception of 3D geometry by learning a normal-adapted diffusion model and a normal-aligned diffusion model. HumanNorm outperforms existing text-to-3D methods in both geometry and texture quality.
  arXiv  Detail & Related papers  (2023-10-02T17:59:17Z)
• HumanLiff: Layer-wise 3D Human Generation with Diffusion Model [55.891036415316876]
  Existing 3D human generative models mainly generate a clothed 3D human as an undetectable 3D model in a single pass. We propose HumanLiff, the first layer-wise 3D human generative model with a unified diffusion process.
  arXiv  Detail & Related papers  (2023-08-18T17:59:04Z)
• Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors [56.192682114114724]
  Get3DHuman is a novel 3D human framework that can significantly boost the realism and diversity of the generated outcomes. Our key observation is that the 3D generator can profit from human-related priors learned through 2D human generators and 3D reconstructors.
  arXiv  Detail & Related papers  (2023-02-02T15:37:46Z)
• THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers [67.8628917474705]
  THUNDR is a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people. We show state-of-the-art results on Human3.6M and 3DPW, for both the fully-supervised and the self-supervised models. We observe very solid 3d reconstruction performance for difficult human poses collected in the wild.
  arXiv  Detail & Related papers  (2021-06-17T09:09:24Z)

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.