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New watermarking technique embeds data directly into 3D Gaussian Splatting parameters

Researchers have developed FlyMark, a novel method for embedding invisible watermarks directly into 3D Gaussian Splatting (3DGS) scene parameters. Unlike previous methods that rely on scene optimization or rendered views, FlyMark embeds evidence within the existing parameters, ensuring its persistence long after the original training tools are gone. The watermarking process utilizes directions derived from a published fruit fly connectome, with a virtual observer reading apparent luminance to encode messages. This approach preserves all scene geometry and appearance parameters identically and allows for extraction using simple queries and voting, demonstrating high accuracy and visual fidelity on various scenes. AI

IMPACT Introduces a method for securing intellectual property in 3D generative models, potentially impacting content ownership and distribution.

RANK_REASON Academic paper detailing a new technical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New watermarking technique embeds data directly into 3D Gaussian Splatting parameters

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Academic paper detailing a new technical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ziyuan Luo, Haoliang Li, Renjie Wan ·

    FlyMark: Training-Free Invisible Watermarking of 3D Gaussian Splatting via a Fruit Fly Connectome

    arXiv:2610.11364v1 Announce Type: new Abstract: A trained 3D Gaussian Splatting (3DGS) scene ships as a portable parameter array that can be copied, pruned, requantized, or repackaged outside its training pipeline, so ownership evidence is most useful when it lives in the release…