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arXiv · 2611.00032

Gaussifier: Fast 2D Gaussian Decomposition

Yunlai Zhou·Yu Yin·Michael W. Jenkins·Yehe Liu
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Abstract

2D Gaussians render images at thousands of frames per second. However, fitting an image takes seconds of optimization. This confines 2D Gaussians to offline use. Feedforward methods are faster but less accurate without per-image fine-tuning. We present Gaussifier, a lightweight feedforward framework for 2D Gaussian decomposition. We learn a density map of Gaussian centers from per-image fitting data. Equal-mass Voronoi places Gaussians so that each Voronoi cell holds approximately equal density. A stateless, reusable network then refines the output in a few iterations. Gaussifier reaches 40.73/38.61\,dB PSNR on Kodak-24/DIV2K at over 60 FPS. At the same Gaussian count, it exceeds every feedforward baseline by 3.0 to 15.6\,dB in PSNR. With fewer Gaussians than any baseline, it still leads the strongest one by 2.5 to 3.5\,dB. At the same count, baselines with their own per-image fitting need 26 to 326 times longer to reach its PSNR. Gaussifier makes high-fidelity 2D Gaussian images fast and light enough for real-time applications. We release code, weights and a browser-based WebGPU demo at https://github.com/Gaussifier/gaussifier.

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Yunlai Zhou, Yu Yin, Michael W. Jenkins, Yehe Liu. 2026-10-06. Gaussifier: Fast 2D Gaussian Decomposition. https://arxiv.org/abs/2611.00032

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