Gaussian Deja-vu: Creating Controllable 3D Gaussian Head-Avatars with Enhanced Generalization and Personalization Abilities

TL;DR

This paper introduces the Gaussian Deja-vu framework for creating controllable 3D Gaussian head avatars that are quickly personalized, outperforming existing methods in quality and efficiency by leveraging a generalized model and expression-aware rectification.

Contribution

The paper presents a novel framework that accelerates 3D Gaussian head avatar creation by combining a generalized model with rapid personalization techniques without neural networks.

Findings

Outperforms state-of-the-art in photorealism

Reduces training time to minutes

Enables rapid personalization without neural networks

Abstract

Recent advancements in 3D Gaussian Splatting (3DGS) have unlocked significant potential for modeling 3D head avatars, providing greater flexibility than mesh-based methods and more efficient rendering compared to NeRF-based approaches. Despite these advancements, the creation of controllable 3DGS-based head avatars remains time-intensive, often requiring tens of minutes to hours. To expedite this process, we here introduce the "Gaussian Deja-vu" framework, which first obtains a generalized model of the head avatar and then personalizes the result. The generalized model is trained on large 2D (synthetic and real) image datasets. This model provides a well-initialized 3D Gaussian head that is further refined using a monocular video to achieve the personalized head avatar. For personalizing, we propose learnable expression-aware rectification blendmaps to correct the initial 3D Gaussians, ensuring rapid convergence without the reliance on neural networks. Experiments demonstrate that the proposed method meets its objectives. 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, producing the avatar in minutes.