FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision

TL;DR

FlexAvatar is a transformer-based method that creates complete 3D head avatars from a single image by effectively combining monocular and multi-view data during training.

Contribution

It introduces learnable data source tokens enabling unified training on monocular and multi-view datasets for high-quality 3D head avatar reconstruction.

Findings

Achieves full 3D head reconstructions from monocular images.

Enables identity interpolation and flexible fitting.

Outperforms existing methods in view extrapolation and facial animation.

Abstract

We introduce FlexAvatar, a method for creating high-quality and complete 3D head avatars from a single image. A core challenge lies in the limited availability of multi-view data and the tendency of monocular training to yield incomplete 3D head reconstructions. We identify the root cause of this issue as the entanglement between driving signal and target viewpoint when learning from monocular videos. To address this, we propose a transformer-based 3D portrait animation model with learnable data source tokens, so-called bias sinks, which enables unified training across monocular and multi-view datasets. This design leverages the strengths of both data sources during inference: strong generalization from monocular data and full 3D completeness from multi-view supervision. Furthermore, our training procedure yields a smooth latent avatar space that facilitates identity interpolation and flexible fitting to an arbitrary number of input observations. In extensive evaluations on single-view, few-shot, and monocular avatar creation tasks, we verify the efficacy of FlexAvatar. Many existing methods struggle with view extrapolation while FlexAvatar generates complete 3D head avatars with realistic facial animations. Website: https://tobias-kirschstein.github.io/flexavatar/