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FlashAvatar: High-fidelity Head Avatar with Efficient Gaussian Embedding

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arxiv 2312.02214 v2 pith:I6GRQLPG submitted 2023-12-03 cs.CV cs.GR

classification cs.CVcs.GR
keywords flashavataravatardetailsfacialgaussianhigh-fidelitymodelrendering
verification ladder T0 review T1 audit T2 compute T3 formal
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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/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar

    cs.GR 2025-07 conditional novelty 7.0 of 10

    GeoAvatar improves 3D head avatar quality by adaptively regulating Gaussian offsets per facial region, adding a detailed mouth structure with part-wise deformation, and releasing a new expressive monocular dataset, Dy...

  2. GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A normal-map-conditioned multi-view head diffusion model generates pseudo-ground-truth views that regularize Gaussian avatar optimization, improving reconstruction of unobserved head regions from monocular videos.

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