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PSAvatar: A Point-based Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting
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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 ).
Forward citations
Cited by 4 Pith papers
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GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
GeoAvatar improves 3D head avatar quality by adaptively regulating Gaussian offsets per facial region, adding a detailed mouth structure with part-wise deformation, and releasing a new expressive monocular dataset, Dy...
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S-Avatar: Diffusion-Guided Gaussian Head Avatars from a Single Image
A three-stage pipeline generates animatable 3D Gaussian head avatars from one image by diffusion-based splat synthesis, FLAME fitting, and inverse-distance binding with scale adaptation.
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AvatarBack: Back-Head Generation for Complete 3D Avatars from Front-View Images
AvatarBack adds a generative back-head prior and a learned spatial alignment to Gaussian-splatting head avatars, improving rear geometry and texture while keeping frontal quality.
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SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting
SyncTalk++ synthesizes speech-driven talking-head videos via 3D Gaussian Splatting and reports state-of-the-art synchronization and quality at up to 101 FPS.
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