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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
3D Head ReconstructionNeRSemble (test)
PSNR23.5
20
Head Avatar ReconstructionINSTA Dataset
PSNR26.98
14
Novel View SynthesisNeRSemble (test)
PSNR31.1
10
3D Head Avatar ReconstructionNHA, NerFace, PointAvatar, INSTA, and custom captures (test)
L1 Error0.012
8
Head Avatar ReconstructionINSTA dataset (test)
PSNR (bala)31.58
8
Head Avatar ReconstructionGaussianBlendShapes (test)
PSNR (Subject 1)32.1
8
Head Avatar ReconstructionPointAvatar Dataset
PSNR24.62
7
Head Avatar ReconstructionNerFace Dataset
PSNR25.74
7
Head Avatar ReconstructionHDTF Dataset
PSNR25.08
7
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