HQ3DAvatar: High Quality Controllable 3D Head Avatar
Abstract
Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A comm- on approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, they often fail to learn complex geometric details such as the mouth interior, hair, and topological changes over time. This paper presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. It leverages multiresolution hash encoding in the learned feature space, allowing for high-quality, faster training and high-resolution rendering. At test time, our method is driven by a monocular RGB video. Here, an image encoder extracts face-specific features that also condition the learnable canonical space. This encourages deformation-dependent texture variations during training. We also propose a novel optical flow based loss that ensures correspondences in the learned canonical space, thus encouraging artifact-free and temporally consistent renderings. We show results on challenging facial expressions and show free-viewpoint renderings at interactive real-time rates for medium image resolutions. Our method outperforms all existing approaches, both visually and numerically. We will release our multiple-identity dataset to encourage further research.
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Citation
@misc{teotia2023hq3davatar, title={HQ3DAvatar: High Quality Controllable 3D Head Avatar}, author={Kartik Teotia and Mallikarjun B R and Xingang Pan and Hyeongwoo Kim and Pablo Garrido and Mohamed Elgharib and Christian Theobalt}, year={2023}, eprint={2303.14471}, archivePrefix={arXiv}, primaryClass={cs.CV} }
Acknowledgments
This work was supported by the ERC Consolidator Grant 4DReply (770784).
Contact
For questions, clarifications, please get in touch with:Kartik Teotia
kteotia@mpi-inf.mpg.de