Gaussian Masked Autoencoders
Keywords: Representation learning, Gaussian Splatting
Abstract: This paper explores Masked Autoencoders (MAE) with Gaussian Splatting. While mainstream self-supervised learning frameworks such as MAE operate on low-level pixels, the image synthesis community has evolved to use latent, mid-level representations for better generative visual data modeling. Our approach, named GMAE, aims to reconcile these two and get the benefits of both worlds. Like MAE, it reconstructs the image end-to-end in the pixel space; however, it also introduces an intermediate, 3D Gaussian-based representation and renders images via splatting. We show that GMAE can enable various zero-shot learning capabilities (e.g figure-ground segmentation, image layering, edge detection, etc) while preserving the high self-supervised representation quality from MAE. Notably, we are the first to employ Gaussian primitives in an image representation learning framework beyond optimization-based single-scene reconstructions. We believe GMAE will inspire further research in this direction and contribute to developing next-generation techniques for modeling high-fidelity visual data.
Supplementary Material: pdf
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 12248
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