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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
41
Novel View SynthesisNeRSemble
SSIM81.3
24
3D Head ReconstructionNeRSemble (test)
PSNR23.5
20
Self-ReenactmentINSTA
PSNR28.11
19
Head Avatar ReconstructionINSTA Dataset
PSNR26.98
14
Self-ReenactmentNeRSemble Novel Expression (all view)
PSNR25.32
13
Self-ReenactmentNeRSemble Novel Expression frontal
PSNR25.94
13
Novel View SynthesisNeRSemble (test)
PSNR31.1
10
Monocular ReenactmentSplattingAvatar
MSE1.075
10
3D Head Avatar ReconstructionNHA, NerFace, PointAvatar, INSTA, and custom captures (test)
L1 Error0.012
8
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