GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation

Jie Wang , Jiu-Cheng Xie , Xianyan Li

IEEE Transactions on Visualization and Computer Graphics +

|

April 17, 2025

View abstract on PubMed

Summary

GaussianHead creates realistic 3D head avatars and animations using anisotropic 3D Gaussians. This novel approach achieves high-fidelity results with a compact model size, outperforming existing methods in head reconstruction and synthesis tasks.

Area Of Science

Computer Vision
Computer Graphics
3D Modeling

Background

Generating lifelike 3D head avatars and animations for diverse subjects is a significant challenge in computer vision.
Existing methods often struggle with capturing intricate head dynamics and detailed textures efficiently.
The need for compact yet high-fidelity representations is crucial for practical applications.

Purpose Of The Study

To introduce GaussianHead, a novel method for modeling active heads using anisotropic 3D Gaussians.
To enable the creation of lifelike 3D head avatars and compelling animations.
To achieve high-fidelity visual results with a compact model size and superior performance compared to state-of-the-art methods.

Main Methods

Modeling the active head using anisotropic 3D Gaussians.
Integrating a motion deformation field and a single-resolution tri-plane for capturing dynamics and texture.
Introducing a customized derivation scheme for precise position transformation and an inherited derivation strategy for expedited training.

Main Results

Achieved high-fidelity visual results with a remarkably compact model size (approximately 12 MB).
Demonstrated superior performance over state-of-the-art alternatives in reconstruction, cross-identity reenactment, and novel view synthesis.
Successfully generated multiple 'doppelgangers' through learnable parameters for precise position transformation.

Conclusions

GaussianHead effectively models active heads, enabling the creation of lifelike 3D avatars and animations.
The method offers a significant advancement in terms of visual fidelity, model compactness, and performance.
GaussianHead represents a promising direction for future research in 3D head modeling and animation.