Ultraman: Single Image 3D Human Reconstruction with Ultra Speed and Detail

• URL: http://arxiv.org/abs/2403.12028v1
• Date: Mon, 18 Mar 2024 17:57:30 GMT
• Title: Ultraman: Single Image 3D Human Reconstruction with Ultra Speed and Detail
• Authors: Mingjin Chen, Junhao Chen, Xiaojun Ye, Huan-ang Gao, Xiaoxue Chen, Zhaoxin Fan, Hao Zhao,
• Abstract summary: 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.
• Score: 11.604919466757003
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D human body reconstruction has been a challenge in the field of computer vision. Previous methods are often time-consuming and difficult to capture the detailed appearance of the human body. In this paper, we propose a new method called \emph{Ultraman} for fast reconstruction of textured 3D human models from a single image. Compared to existing techniques, \emph{Ultraman} greatly improves the reconstruction speed and accuracy while preserving high-quality texture details. We present a set of new frameworks for human reconstruction consisting of three parts, geometric reconstruction, texture generation and texture mapping. Firstly, a mesh reconstruction framework is used, which accurately extracts 3D human shapes from a single image. At the same time, we propose a method to generate a multi-view consistent image of the human body based on a single image. This is finally combined with a novel texture mapping method to optimize texture details and ensure color consistency during reconstruction. Through extensive experiments and evaluations, we demonstrate the superior performance of \emph{Ultraman} on various standard datasets. In addition, \emph{Ultraman} outperforms state-of-the-art methods in terms of human rendering quality and speed. Upon acceptance of the article, we will make the code and data publicly available.

Related papers

• GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement [51.97726804507328]
  We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images.
  arXiv  Detail & Related papers  (2024-06-09T05:19:24Z)
• ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis [49.28239918969784]
  We introduce a texture-consistent back view synthesis module that could transfer the reference image content to the back view. We also propose a visibility-aware patch consistency regularization for texture mapping and refinement combined with the synthesized back view texture.
  arXiv  Detail & Related papers  (2023-11-28T13:55:53Z)
• 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)
• 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)
• ReFu: Refine and Fuse the Unobserved View for Detail-Preserving Single-Image 3D Human Reconstruction [31.782985891629448]
  Single-image 3D human reconstruction aims to reconstruct the 3D textured surface of the human body given a single image. We propose ReFu, a coarse-to-fine approach that refines the projected backside view image and fuses the refined image to predict the final human body.
  arXiv  Detail & Related papers  (2022-11-09T09:14:11Z)
• 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)
• 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)
• 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)
• Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction [76.1612334630256]
  We harness the power of Generative Adversarial Networks (GANs) and Deep Convolutional Neural Networks (DCNNs) to reconstruct the facial texture and shape from single images. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, facial texture reconstruction with high-frequency details.
  arXiv  Detail & Related papers  (2021-05-16T16:35:44Z)
• Neural Re-Rendering of Humans from a Single Image [80.53438609047896]
  We propose a new method for neural re-rendering of a human under a novel user-defined pose and viewpoint. Our algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image.
  arXiv  Detail & Related papers  (2021-01-11T18:53:47Z)
• 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)

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.