[Submitted on 16 Oct 2026]
Title:EGGHead: Efficient Generalizable Gaussian Heads from a Single Image
View a PDF of the paper titled EGGHead: Efficient Generalizable Gaussian Heads from a Single Image, by Pilseo Park and 5 other authors
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Abstract:Creating photorealistic human heads typically involves trade-offs between representation power, efficiency, and generalizability. Traditional mesh-based solutions relying on 3DMMs generally sacrifice representation power but are efficient to render. On the other hand, NeRF-based methods improve representation power and fidelity while sacrificing efficiency. Gaussian splatting methods attempt to resolve these trade-offs by offering both high representation power and efficiency with real-time rendering. However, their efficiency nonetheless depends on the number of rasterized Gaussians, and the time needed to optimize and prepare the head (the enrollment time) is often high. In light of this, we propose EGGHead, which introduces a novel Gaussian allocation algorithm that efficiently assigns Gaussians to the head's semantic regions given a fixed Gaussian budget. Our algorithm reduces the number of Gaussians needed while minimizing the impact on representation power. EGGHead operates in a single forward pass and outperforms previous single-image face modeling methods in reconstruction quality, novel view synthesis, and rendering speeds.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.26898 [cs.CV]
(or arXiv:2610.26898v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.26898
arXiv-issued DOI via DataCite
Submission history
From: Pilseo Park [view email] [v1] Fri, 16 Oct 2026 19:08:10 UTC (43,888 KB)
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