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GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians

About

We introduce GaussianAvatars, a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model, e.g., through expression transfer from a driving sequence or by manually changing the morphable model parameters. We parameterize each splat by a local coordinate frame of a triangle and optimize for explicit displacement offset to obtain a more accurate geometric representation. During avatar reconstruction, we jointly optimize for the morphable model parameters and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios. For instance, we show reenactments from a driving video, where our method outperforms existing works by a significant margin.

Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nie{\ss}ner• 2023

Related benchmarks

TaskDatasetResultRank
Novel Expression SynthesisNeRSemble
PSNR34.94
30
Head Avatar ReconstructionINSTA dataset (test)
PSNR (bala)31.58
8
Head Avatar ReconstructionGaussianBlendShapes (test)
PSNR (Subject 1)32.1
8
Novel View SynthesisNeRSemble (test)
PSNR27.54
6
Head Avatar Reconstruction and RenderingHead Avatar Reconstruction
Training Time (min)14
6
Monocular Facial Avatar ReconstructionOverall Combined Datasets (test)
PSNR26.2
6
Monocular Facial Avatar ReconstructionPointAvatar Dataset (test)
PSNR24.51
6
Monocular Facial Avatar ReconstructionNerFace Dataset (test)
PSNR29.06
6
Monocular Facial Avatar ReconstructionOurs Dataset (test)
PSNR24.18
6
3D Talking Head Super-ResolutionFFHQ (test)
CSIM0.922
5
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