Synthetic Prior for Few-Shot Drivable Head Avatar Inversion

TL;DR

SynShot introduces a synthetic prior-based method for few-shot head avatar inversion, enabling photorealistic, controllable 3D head models that generalize well to new views and expressions, overcoming data and generalization challenges.

Contribution

The paper proposes a synthetic data-driven prior for few-shot head avatar inversion, improving generalization and privacy compliance over existing methods.

Findings

Outperforms state-of-the-art methods in novel view synthesis

Enables photorealistic head avatar generation with few input images

Effectively bridges the domain gap using synthetic priors

Abstract

We present SynShot, a novel method for the few-shot inversion of a drivable head avatar based on a synthetic prior. We tackle three major challenges. First, training a controllable 3D generative network requires a large number of diverse sequences, for which pairs of images and high-quality tracked meshes are not always available. Second, the use of real data is strictly regulated (e.g., under the General Data Protection Regulation, which mandates frequent deletion of models and data to accommodate a situation when a participant's consent is withdrawn). Synthetic data, free from these constraints, is an appealing alternative. Third, state-of-the-art monocular avatar models struggle to generalize to new views and expressions, lacking a strong prior and often overfitting to a specific viewpoint distribution. Inspired by machine learning models trained solely on synthetic data, we propose a method that learns a prior model from a large dataset of synthetic heads with diverse identities, expressions, and viewpoints. With few input images, SynShot fine-tunes the pretrained synthetic prior to bridge the domain gap, modeling a photorealistic head avatar that generalizes to novel expressions and viewpoints. We model the head avatar using 3D Gaussian splatting and a convolutional encoder-decoder that outputs Gaussian parameters in UV texture space. To account for the different modeling complexities over parts of the head (e.g., skin vs hair), we embed the prior with explicit control for upsampling the number of per-part primitives. Compared to SOTA monocular and GAN-based methods, SynShot significantly improves novel view and expression synthesis.