GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation

• URL: http://arxiv.org/abs/2312.01632v4
• Date: Thu, 30 May 2024 02:19:25 GMT
• Title: GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation
• Authors: Jie Wang, Jiu-Cheng Xie, Xianyan Li, Feng Xu, Chi-Man Pun, Hao Gao,
• Abstract summary: This paper presents a framework to model the actional human head with anisotropic 3D Gaussians. In experiments, our method can produce high-fidelity renderings, outperforming state-of-the-art approaches in reconstruction, cross-identity reenactment, and novel view synthesis tasks.
• Score: 35.39887092268696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Constructing vivid 3D head avatars for given subjects and realizing a series of animations on them is valuable yet challenging. This paper presents GaussianHead, which models the actional human head with anisotropic 3D Gaussians. In our framework, a motion deformation field and multi-resolution tri-plane are constructed respectively to deal with the head's dynamic geometry and complex texture. Notably, we impose an exclusive derivation scheme on each Gaussian, which generates its multiple doppelgangers through a set of learnable parameters for position transformation. With this design, we can compactly and accurately encode the appearance information of Gaussians, even those fitting the head's particular components with sophisticated structures. In addition, an inherited derivation strategy for newly added Gaussians is adopted to facilitate training acceleration. Extensive experiments show that our method can produce high-fidelity renderings, outperforming state-of-the-art approaches in reconstruction, cross-identity reenactment, and novel view synthesis tasks. Our code is available at: https://github.com/chiehwangs/gaussian-head.

Related papers

• Generalizable and Animatable Gaussian Head Avatar [50.34788590904843]
  We propose Generalizable and Animatable Gaussian head Avatar (GAGAvatar) for one-shot animatable head avatar reconstruction. We generate the parameters of 3D Gaussians from a single image in a single forward pass. Our method exhibits superior performance compared to previous methods in terms of reconstruction quality and expression accuracy.
  arXiv  Detail & Related papers  (2024-10-10T14:29:00Z)
• 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)
• Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting [33.01987451251659]
  3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruction. Despite its potential, 3DGS encounters challenges, including needle-like artifacts, suboptimal geometries, and inaccurate normals. We introduce effective rank as a regularization, which constrains the structure of the Gaussians.
  arXiv  Detail & Related papers  (2024-06-17T15:51:59Z)
• Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)
• Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
  It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion. We develop a novel GS-based method that enables interactive deformation. Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
  arXiv  Detail & Related papers  (2024-02-07T12:36:54Z)
• GaussianStyle: Gaussian Head Avatar via StyleGAN [64.85782838199427]
  We propose a novel framework that integrates the volumetric strengths of 3DGS with the powerful implicit representation of StyleGAN. We show that our method achieves state-of-the-art performance in reenactment, novel view synthesis, and animation.
  arXiv  Detail & Related papers  (2024-02-01T18:14:42Z)
• GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning [60.33970027554299]
  Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts.
  arXiv  Detail & Related papers  (2023-12-18T18:59:12Z)
• Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians [41.86540576028268]
  We propose controllable 3D Gaussian Head Avatars for lightweight sparse-view setups. We show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.
  arXiv  Detail & Related papers  (2023-12-05T11:01:44Z)

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.