Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM

• URL: http://arxiv.org/abs/2401.12175v2
• Date: Thu, 14 Mar 2024 08:12:46 GMT
• Title: Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM
• Authors: Zhenzhen Weng, Jingyuan Liu, Hao Tan, Zhan Xu, Yang Zhou, Serena Yeung-Levy, Jimei Yang,
• Abstract summary: We present Human-LRM, a diffusion-guided feed-forward model that predicts the implicit field of a human from a single image. Our method is able to capture human without any template prior, e.g., SMPL, and effectively enhance occluded parts with rich and realistic details.
• Score: 29.13412037370585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing 3D humans from a single image has been extensively investigated. However, existing approaches often fall short on capturing fine geometry and appearance details, hallucinating occluded parts with plausible details, and achieving generalization across unseen and in-the-wild datasets. We present Human-LRM, a diffusion-guided feed-forward model that predicts the implicit field of a human from a single image. Leveraging the power of the state-of-the-art reconstruction model (i.e., LRM) and generative model (i.e Stable Diffusion), our method is able to capture human without any template prior, e.g., SMPL, and effectively enhance occluded parts with rich and realistic details. Our approach first uses a single-view LRM model with an enhanced geometry decoder to get the triplane NeRF representation. The novel view renderings from the triplane NeRF provide strong geometry and color prior, from which we generate photo-realistic details for the occluded parts using a diffusion model. The generated multiple views then enable reconstruction with high-quality geometry and appearance, leading to superior overall performance comparing to all existing human reconstruction methods.

Related papers

• 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)
• MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement [23.707586182294932]
  Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge. We introduce MagicMan, a human-specific multi-view diffusion model designed to generate high-quality novel view images from a single reference image.
  arXiv  Detail & Related papers  (2024-08-26T12:10:52Z)
• Generalizable Human Gaussians from Single-View Image [52.100234836129786]
  We introduce a single-view generalizable Human Gaussian Model (HGM) Our approach uses a ControlNet to refine rendered back-view images from coarse predicted human Gaussians. To mitigate the potential generation of unrealistic human poses and shapes, we incorporate human priors from the SMPL-X model as a dual branch.
  arXiv  Detail & Related papers  (2024-06-10T06:38:11Z)
• 3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models [52.96248836582542]
  We propose an effective approach based on recent diffusion models, termed HumanWild, which can effortlessly generate human images and corresponding 3D mesh annotations. By exclusively employing generative models, we generate large-scale in-the-wild human images and high-quality annotations, eliminating the need for real-world data collection.
  arXiv  Detail & Related papers  (2024-03-17T06:31:16Z)
• Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction [77.69363640021503]
  3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. We present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects.
  arXiv  Detail & Related papers  (2023-04-13T17:59:01Z)
• GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images [79.39247661907397]
  We introduce an effective framework Generalizable Model-based Neural Radiance Fields to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy.
  arXiv  Detail & Related papers  (2023-03-24T03:32:02Z)
• NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors [24.05480789681139]
  We propose NeRDi, a single-view NeRF synthesis framework with general image priors from 2D diffusion models. We leverage off-the-shelf vision-language models and introduce a two-section language guidance as conditioning inputs to the diffusion model. We also demonstrate our generalizability in zero-shot NeRF synthesis for in-the-wild images.
  arXiv  Detail & Related papers  (2022-12-06T19:00:07Z)
• NeuralReshaper: Single-image Human-body Retouching with Deep Neural Networks [50.40798258968408]
  We present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks. Our approach follows a fit-then-reshape pipeline, which first fits a parametric 3D human model to a source human image. To deal with the lack-of-data problem that no paired data exist, we introduce a novel self-supervised strategy to train our network.
  arXiv  Detail & Related papers  (2022-03-20T09:02:13Z)
• 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data [77.57798334776353]
  We consider the problem of obtaining dense 3D reconstructions of humans from single and partially occluded views. We suggest that ambiguities can be modelled more effectively by parametrizing the possible body shapes and poses. We show that our method outperforms alternative approaches in ambiguous pose recovery on standard benchmarks for 3D humans.
  arXiv  Detail & Related papers  (2020-11-02T13:55:31Z)

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.