SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance

• URL: http://arxiv.org/abs/2312.08889v2
• Date: Tue, 26 Dec 2023 10:20:09 GMT
• Title: SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance
• Authors: Yuanyou Xu, Zongxin Yang, Yi Yang
• Abstract summary: We present SEEAvatar, a method for generating photorealistic 3D avatars from text. For geometry, we propose to constrain the optimized avatar in a decent global shape with a template avatar. For appearance generation, we use diffusion model enhanced by prompt engineering to guide a physically based rendering pipeline.
• Score: 37.85026590250023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Powered by large-scale text-to-image generation models, text-to-3D avatar generation has made promising progress. However, most methods fail to produce photorealistic results, limited by imprecise geometry and low-quality appearance. Towards more practical avatar generation, we present SEEAvatar, a method for generating photorealistic 3D avatars from text with SElf-Evolving constraints for decoupled geometry and appearance. For geometry, we propose to constrain the optimized avatar in a decent global shape with a template avatar. The template avatar is initialized with human prior and can be updated by the optimized avatar periodically as an evolving template, which enables more flexible shape generation. Besides, the geometry is also constrained by the static human prior in local parts like face and hands to maintain the delicate structures. For appearance generation, we use diffusion model enhanced by prompt engineering to guide a physically based rendering pipeline to generate realistic textures. The lightness constraint is applied on the albedo texture to suppress incorrect lighting effect. Experiments show that our method outperforms previous methods on both global and local geometry and appearance quality by a large margin. Since our method can produce high-quality meshes and textures, such assets can be directly applied in classic graphics pipeline for realistic rendering under any lighting condition. Project page at: https://yoxu515.github.io/SEEAvatar/.

Related papers

• UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures [80.047065473698]
  We propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry, and superior quality of physically based rendering (PBR) textures without unwanted lighting. We demonstrate the effectiveness and robustness of the proposed method, outperforming the state-of-the-art methods by a large margin in the experiments.
  arXiv  Detail & Related papers  (2024-01-20T01:55:17Z)
• 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)
• Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models [107.84324544272481]
  The ability to generate diverse 3D articulated head avatars is vital to a plethora of applications, including augmented reality, cinematography, and education. Recent work on text-guided 3D object generation has shown great promise in addressing these needs. We show that our diffusion-based articulated head avatars outperform state-of-the-art approaches for this task.
  arXiv  Detail & Related papers  (2023-07-10T19:15:32Z)
• Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization [91.52882218901627]
  We propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T17:58:40Z)
• AvatarGen: A 3D Generative Model for Animatable Human Avatars [108.11137221845352]
  AvatarGen is an unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries. Our method can generate animatable 3D human avatars with high-quality appearance and geometry modeling. It is competent for many applications, e.g., single-view reconstruction, re-animation, and text-guided synthesis/editing.
  arXiv  Detail & Related papers  (2022-11-26T15:15:45Z)
• SMPLpix: Neural Avatars from 3D Human Models [56.85115800735619]
  We bridge the gap between classic rendering and the latest generative networks operating in pixel space. We train a network that directly converts a sparse set of 3D mesh vertices into photorealistic images. We show the advantage over conventional differentiables both in terms of the level of photorealism and rendering efficiency.
  arXiv  Detail & Related papers  (2020-08-16T10:22:00Z)

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.