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

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. Project homepage: https://peizhiyan.github.io/docs/dejavu