MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing

• URL: http://arxiv.org/abs/2404.19026v1
• Date: Mon, 29 Apr 2024 18:10:12 GMT
• Title: MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing
• Authors: Cong Wang, Di Kang, He-Yi Sun, Shen-Han Qian, Zi-Xuan Wang, Linchao Bao, Song-Hai Zhang,
• Abstract summary: We propose a Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components with more suitable representations. MeGA generates higher-fidelity renderings for the whole head and naturally supports more downstream tasks. Experiments on the NeRSemble dataset demonstrate the effectiveness of our designs.
• Score: 34.31657241047574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating high-fidelity head avatars from multi-view videos is a core issue for many AR/VR applications. However, existing methods usually struggle to obtain high-quality renderings for all different head components simultaneously since they use one single representation to model components with drastically different characteristics (e.g., skin vs. hair). In this paper, we propose a Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components with more suitable representations. Specifically, we select an enhanced FLAME mesh as our facial representation and predict a UV displacement map to provide per-vertex offsets for improved personalized geometric details. To achieve photorealistic renderings, we obtain facial colors using deferred neural rendering and disentangle neural textures into three meaningful parts. For hair modeling, we first build a static canonical hair using 3D Gaussian Splatting. A rigid transformation and an MLP-based deformation field are further applied to handle complex dynamic expressions. Combined with our occlusion-aware blending, MeGA generates higher-fidelity renderings for the whole head and naturally supports more downstream tasks. Experiments on the NeRSemble dataset demonstrate the effectiveness of our designs, outperforming previous state-of-the-art methods and supporting various editing functionalities, including hairstyle alteration and texture editing.

Related papers

• Generating Editable Head Avatars with 3D Gaussian GANs [57.51487984425395]
  Traditional 3D-aware generative adversarial networks (GANs) achieve photorealistic and view-consistent 3D head synthesis. We propose a novel approach that enhances the editability and animation control of 3D head avatars by incorporating 3D Gaussian Splatting (3DGS) as an explicit 3D representation. Our approach delivers high-quality 3D-aware synthesis with state-of-the-art controllability.
  arXiv  Detail & Related papers  (2024-12-26T10:10:03Z)
• 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)
• NPGA: Neural Parametric Gaussian Avatars [46.52887358194364]
  We propose a data-driven approach to create high-fidelity controllable avatars from multi-view video recordings. We build our method around 3D Gaussian splatting for its highly efficient rendering and to inherit the topological flexibility of point clouds. We evaluate our method on the public NeRSemble dataset, demonstrating that NPGA significantly outperforms the previous state-of-the-art avatars on the self-reenactment task by 2.6 PSNR.
  arXiv  Detail & Related papers  (2024-05-29T17:58:09Z)
• Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping [12.308192525760667]
  Existing methods only reconstruct a head without the body, substantially limiting their application scenarios. We propose to model the body part with a neural texture that consists of coarse and pose-dependent fine colors. We demonstrate that Gaussian Head & Shoulders can fit the high-frequency details on the clothed upper body with high fidelity and potentially improve the accuracy and fidelity of the head region.
  arXiv  Detail & Related papers  (2024-05-20T14:39:49Z)
• GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image [89.70322127648349]
  We propose a generic avatar editing approach that can be universally applied to various 3DMM driving volumetric head avatars. To achieve this goal, we design a novel expression-aware modification generative model, which enables lift 2D editing from a single image to a consistent 3D modification field.
  arXiv  Detail & Related papers  (2024-04-02T17:58:35Z)
• Hybrid Explicit Representation for Ultra-Realistic Head Avatars [55.829497543262214]
  We introduce a novel approach to creating ultra-realistic head avatars and rendering them in real-time. UV-mapped 3D mesh is utilized to capture sharp and rich textures on smooth surfaces, while 3D Gaussian Splatting is employed to represent complex geometric structures. Experiments that our modeled results exceed those of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-03-18T04:01:26Z)
• PSAvatar: A Point-based Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting [17.78639236586134]
  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.
  arXiv  Detail & Related papers  (2024-01-23T16:40:47Z)
• HHAvatar: Gaussian Head Avatar with Dynamic Hairs [27.20228210350169]
  We proposeAvatar represented by controllable 3D Gaussians for high-fidelity head avatar with dynamic hair modeling. Our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution.
  arXiv  Detail & Related papers  (2023-12-05T11:01:44Z)
• HQ3DAvatar: High Quality Controllable 3D Head Avatar [65.70885416855782]
  This paper presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. At test time, our method is driven by a monocular RGB video.
  arXiv  Detail & Related papers  (2023-03-25T13:56:33Z)
• HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars [65.82222842213577]
  We propose a novel neural rendering pipeline, which synthesizes virtual human avatars from arbitrary poses efficiently and at high quality. First, we learn to encode articulated human motions on a dense UV manifold of the human body surface. We then leverage the encoded information on the UV manifold to construct a 3D volumetric representation.
  arXiv  Detail & Related papers  (2021-12-19T17:34:15Z)

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.