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FlashAvatar: High-fidelity Head Avatar with Efficient Gaussian Embedding

About

We propose FlashAvatar, a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this, we maintain a uniform 3D Gaussian field embedded in the surface of a parametric face model and learn extra spatial offset to model non-surface regions and subtle facial details. While full use of geometric priors can capture high-frequency facial details and preserve exaggerated expressions, proper initialization can help reduce the number of Gaussians, thus enabling super-fast rendering speed. Extensive experimental results demonstrate that FlashAvatar outperforms existing works regarding visual quality and personalized details and is almost an order of magnitude faster in rendering speed. Project page: https://ustc3dv.github.io/FlashAvatar/

Jun Xiang, Xuan Gao, Yudong Guo, Juyong Zhang• 2023

Related benchmarks

TaskDatasetResultRank
Self-ReenactmentINSTA
PSNR29.13
14
Self-ReenactmentHDTF
PSNR27.58
14
Head Avatar ReconstructionINSTA dataset (test)
PSNR (bala)31.98
8
Monocular 3D Head Avatar CreationNeRSemble
PSNR16.3
8
Head Avatar ReconstructionGaussianBlendShapes (test)
PSNR (Subject 1)29.67
8
Head Avatar RenderingINSTA
Inverse MAE88.3
7
Head Avatar Reconstruction and RenderingHead Avatar Reconstruction
Training Time (min)17
6
Monocular Facial Avatar ReconstructionOurs Dataset (test)
PSNR25.48
6
Self-Reenactmentself-captured dataset
PSNR27.46
6
3D-aware face reconstructionNerFACE (test)
PSNR26.883
6
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