PSAvatar: A Point-based Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2401.12900v5
- Date: Mon, 24 Jun 2024 02:30:09 GMT
- Title: PSAvatar: A Point-based Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting
- Authors: Zhongyuan Zhao, Zhenyu Bao, Qing Li, Guoping Qiu, Kanglin Liu,
- Abstract summary: PSAvatar is a novel framework for animatable head avatar creation.
It employs 3D Gaussian for fine detail representation and high fidelity rendering.
We show that PSAvatar can reconstruct high-fidelity head avatars of a variety of subjects and the avatars can be animated in real-time.
- Score: 17.78639236586134
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Despite much progress, achieving real-time high-fidelity head avatar animation is still difficult and existing methods have to trade-off between speed and quality. 3DMM based methods often fail to model non-facial structures such as eyeglasses and hairstyles, while neural implicit models suffer from deformation inflexibility and rendering inefficiency. Although 3D Gaussian has been demonstrated to possess promising capability for geometry representation and radiance field reconstruction, applying 3D Gaussian in head avatar creation remains a major challenge since it is difficult for 3D Gaussian to model the head shape variations caused by changing poses and expressions. In this paper, we introduce PSAvatar, a novel framework for animatable head avatar creation that utilizes discrete geometric primitive to create a parametric morphable shape model and employs 3D Gaussian for fine detail representation and high fidelity rendering. The parametric morphable shape model is a Point-based Morphable Shape Model (PMSM) which uses points instead of meshes for 3D representation to achieve enhanced representation flexibility. The PMSM first converts the FLAME mesh to points by sampling on the surfaces as well as off the meshes to enable the reconstruction of not only surface-like structures but also complex geometries such as eyeglasses and hairstyles. By aligning these points with the head shape in an analysis-by-synthesis manner, the PMSM makes it possible to utilize 3D Gaussian for fine detail representation and appearance modeling, thus enabling the creation of high-fidelity avatars. We show that PSAvatar can reconstruct high-fidelity head avatars of a variety of subjects and the avatars can be animated in real-time ($\ge$ 25 fps at a resolution of 512 $\times$ 512 ).
Related papers
- Gaussian Deja-vu: Creating Controllable 3D Gaussian Head-Avatars with Enhanced Generalization and Personalization Abilities [10.816370283498287]
We introduce the "Gaussian Deja-vu" framework, which first obtains a generalized model of the head avatar and then personalizes the result.
For personalizing, we propose learnable expression-aware rectification blendmaps, ensuring rapid convergence without the reliance on neural networks.
It outperforms state-of-the-art 3D Gaussian head avatars in terms of photorealistic quality as well as reduces training time consumption to at least a quarter of the existing methods.
arXiv Detail & Related papers (2024-09-23T00:11:30Z) - GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction [47.113910048252805]
High-fidelity 3D human head avatars are crucial for applications in VR/AR, digital human, and film production.
Recent advances have leveraged morphable face models to generate animated head avatars, representing varying identities and expressions.
We introduce 3D Gaussian Parametric Head Model, which employs 3D Gaussians to accurately represent the complexities of the human head.
arXiv Detail & Related papers (2024-07-21T06:03:11Z) - SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded
Gaussian Splatting [26.849406891462557]
We present SplattingAvatar, a hybrid 3D representation of human avatars with Gaussian Splatting embedded on a triangle mesh.
SplattingAvatar renders over 300 FPS on a modern GPU and 30 FPS on a mobile device.
arXiv Detail & Related papers (2024-03-08T06:28:09Z) - MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar [44.125711148560605]
MonoGaussianAvatar is a novel approach that harnesses 3D Gaussian point representation and a Gaussian deformation field to learn explicit head avatars from monocular portrait videos.
Experiments demonstrate the superior performance of our method, which achieves state-of-the-art results among previous methods.
arXiv Detail & Related papers (2023-12-07T18:59:31Z) - GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians [41.378083782290545]
We introduce a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint.
The core idea is a dynamic 3D representation based on 3D Gaussian splats rigged to a parametric morphable face model.
We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios.
arXiv Detail & Related papers (2023-12-04T17:28:35Z) - 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) - PointAvatar: Deformable Point-based Head Avatars from Videos [103.43941945044294]
PointAvatar is a deformable point-based representation that disentangles the source color into intrinsic albedo and normal-dependent shading.
We show that our method is able to generate animatable 3D avatars using monocular videos from multiple sources.
arXiv Detail & Related papers (2022-12-16T10:05:31Z) - 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) - 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.