3D-Aware Semantic-Guided Generative Model for Human Synthesis

• URL: http://arxiv.org/abs/2112.01422v1
• Date: Thu, 2 Dec 2021 17:10:53 GMT
• Title: 3D-Aware Semantic-Guided Generative Model for Human Synthesis
• Authors: Jichao Zhang, Enver Sangineto, Hao Tang, Aliaksandr Siarohin, Zhun Zhong, Nicu Sebe, Wei Wang
• Abstract summary: This paper proposes a 3D-aware Semantic-Guided Generative Model (3D-SGAN) for human image synthesis. Our experiments on the DeepFashion dataset show that 3D-SGAN significantly outperforms the most recent baselines.
• Score: 67.86621343494998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D representations from 2D images, have recently been shown to produce realistic images representing rigid objects, such as human faces or cars. However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications. This paper proposes a 3D-aware Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which integrates a GNeRF and a texture generator. The former learns an implicit 3D representation of the human body and outputs a set of 2D semantic segmentation masks. The latter transforms these semantic masks into a real image, adding a realistic texture to the human appearance. Without requiring additional 3D information, our model can learn 3D human representations with a photo-realistic controllable generation. Our experiments on the DeepFashion dataset show that 3D-SGAN significantly outperforms the most recent baselines.

Related papers

• FAMOUS: High-Fidelity Monocular 3D Human Digitization Using View Synthesis [51.193297565630886]
  The challenge of accurately inferring texture remains, particularly in obscured areas such as the back of a person in frontal-view images. This limitation in texture prediction largely stems from the scarcity of large-scale and diverse 3D datasets. We propose leveraging extensive 2D fashion datasets to enhance both texture and shape prediction in 3D human digitization.
  arXiv  Detail & Related papers  (2024-10-13T01:25:05Z)
• 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)
• InceptionHuman: Controllable Prompt-to-NeRF for Photorealistic 3D Human Generation [61.62346472443454]
  InceptionHuman is a prompt-to-NeRF framework that allows easy control via a combination of prompts in different modalities to generate photorealistic 3D humans. InceptionHuman achieves consistent 3D human generation within a progressively refined NeRF space.
  arXiv  Detail & Related papers  (2023-11-27T15:49:41Z)
• Single-Image 3D Human Digitization with Shape-Guided Diffusion [31.99621159464388]
  NeRF and its variants typically require videos or images from different viewpoints. We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image.
  arXiv  Detail & Related papers  (2023-11-15T18:59:56Z)
• GETAvatar: Generative Textured Meshes for Animatable Human Avatars [69.56959932421057]
  We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality geometries and textures. We propose GETAvatar, a Generative model that directly generates Explicit Textured 3D rendering for animatable human Avatar.
  arXiv  Detail & Related papers  (2023-10-04T10:30:24Z)
• AG3D: Learning to Generate 3D Avatars from 2D Image Collections [96.28021214088746]
  We propose a new adversarial generative model of realistic 3D people from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance.
  arXiv  Detail & Related papers  (2023-05-03T17:56:24Z)
• 3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping [37.14866512377012]
  3DHumanGAN is a 3D-aware generative adversarial network that synthesizes photorealistic images of full-body humans. We propose a novel generator architecture in which a 2D convolutional backbone is modulated by a 3D pose mapping network.
  arXiv  Detail & Related papers  (2022-12-14T17:59:03Z)
• Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars [36.4402388864691]
  3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance fields either explicitly or implicitly. We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images.
  arXiv  Detail & Related papers  (2022-11-21T06:40:46Z)
• EVA3D: Compositional 3D Human Generation from 2D Image Collections [27.70991135165909]
  EVA3D is an unconditional 3D human generative model learned from 2D image collections only. It can sample 3D humans with detailed geometry and render high-quality images (up to 512x256) without bells and whistles. It achieves state-of-the-art 3D human generation performance regarding both geometry and texture quality.
  arXiv  Detail & Related papers  (2022-10-10T17:59:31Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)

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.