Image-GS: Content-Adaptive Image Representation via 2D Gaussians

Abstract

Neural image representations have emerged as a promising approach for encoding and rendering visual data. Combined with learning-based workflows, they demonstrate impressive trade-offs between visual fidelity and memory footprint. Existing methods in this domain, however, often rely on fixed data structures that suboptimally allocate memory or compute-intensive implicit models, hindering their practicality for real-time graphics applications.

Inspired by recent advancements in radiance field rendering, we introduce Image-GS, a content-adaptive image representation based on 2D Gaussians. Leveraging a custom differentiable renderer, Image-GS reconstructs images by adaptively allocating and progressively optimizing a group of anisotropic, colored 2D Gaussians. It achieves a favorable balance between visual fidelity and memory efficiency across a variety of stylized images frequently seen in graphics workflows, especially for those showing non-uniformly distributed features and in low-bitrate regimes. Moreover, it supports hardware-friendly rapid random access for real-time usage, requiring only 0.3K MACs to decode a pixel. Through error-guided progressive optimization, Image-GS naturally constructs a smooth level-of-detail hierarchy. We demonstrate its versatility with several applications, including texture compression, semantics-aware compression, and joint image compression and restoration.

Acknowledgement

This research is partially supported by the NSF grants #2232817 and #2225861, and an Intel-sponsored research program.

Bibtex

@inproceedings{zhang2025image,
title={Image-GS: Content-Adaptive Image Representation via 2D Gaussians},
author={Zhang, Yunxiang and Kuznetsov, Alexandr and Jindal, Akshay and Chen, Kenneth and Sochenov, Anton and Kaplanyan, Anton and Sun, Qi},
booktitle={ACM SIGGRAPH 2025 Conference Proceedings},
pages={1–20},
year={2025}
}