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

• URL: http://arxiv.org/abs/2407.01866v1
• Date: Tue, 2 Jul 2024 00:45:21 GMT
• Title: Image-GS: Content-Adaptive Image Representation via 2D Gaussians
• Authors: Yunxiang Zhang, Alexandr Kuznetsov, Akshay Jindal, Kenneth Chen, Anton Sochenov, Anton Kaplanyan, Qi Sun,
• Abstract summary: We propose Image-GS, a content-adaptive image representation. Using anisotropic 2D Gaussians as the basis, Image-GS shows high memory efficiency, supports fast random access, and offers a natural level of detail stack. General efficiency and fidelity of Image-GS are validated against several recent neural image representations and industry-standard texture compressors. We hope this research offers insights for developing new applications that require adaptive quality and resource control, such as machine perception, asset streaming, and content generation.
• Score: 55.15950594752051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural image representations have recently emerged as a promising technique for storing, streaming, and rendering visual data. Coupled with learning-based workflows, these novel representations have demonstrated remarkable visual fidelity and memory efficiency. However, existing neural image representations often rely on explicit uniform data structures without content adaptivity or computation-intensive implicit models, limiting their adoption in real-time graphics applications. Inspired by recent advances in radiance field rendering, we propose Image-GS, a content-adaptive image representation. Using anisotropic 2D Gaussians as the basis, Image-GS shows high memory efficiency, supports fast random access, and offers a natural level of detail stack. Leveraging a tailored differentiable renderer, Image-GS fits a target image by adaptively allocating and progressively optimizing a set of 2D Gaussians. The generalizable efficiency and fidelity of Image-GS are validated against several recent neural image representations and industry-standard texture compressors on a diverse set of images. Notably, its memory and computation requirements solely depend on and linearly scale with the number of 2D Gaussians, providing flexible controls over the trade-off between visual fidelity and run-time efficiency. We hope this research offers insights for developing new applications that require adaptive quality and resource control, such as machine perception, asset streaming, and content generation.

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