RAW: Robust Avatar Watermarking -- Benchmarking and Baseline
Digital avatar watermarking faces unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. This paper introduces the RAW benchmark with 50 synthetic avatar videos from 5 providers and 6 attacks simulating real-world workflows. Evaluation of 7 existing methods reveals that avatar-specific attacks degrade watermark recovery. The proposed WALT method embeds watermarks in UV texture space via 3D face reconstruction, achieving 92.4% robustness to zoom and 95.6% on background removal. The benchmark is released to facilitate research.
Article intelligence
Key points
- Avatar watermarking faces challenges like background removal and reframing.
- RAW benchmark includes 50 synthetic avatar videos and 6 attacks.
- Existing methods perform poorly under avatar-specific attacks.
- WALT uses UV texture space embedding for high robustness.
Why it matters
This matters because avatar watermarking faces challenges like background removal and reframing.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.23994] RAW: Robust Avatar Watermarking -- Benchmarking and Baseline
[Submitted on 17 May 2026]
Title:RAW: Robust Avatar Watermarking -- Benchmarking and Baseline
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Abstract:Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. We introduce \textbf{RAW} (Robust Avatar Watermarking), a benchmark comprising 50 synthetic avatar videos from 5 commercial providers and 6 attacks simulating real-world avatar workflows. Evaluating 7 existing methods reveals that avatar-specific attacks such as background removal significantly degrade watermark recovery. We propose \textbf{WALT} (Watermarking Avatars with Learned Textures), which embeds watermarks in UV texture space via 3D face reconstruction. WALT achieves the highest robustness to zoom attacks (92.4\%) while maintaining strong performance on background removal (95.6\%). We release our benchmark to facilitate research into avatar-specific watermarking.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.23994 [cs.CV]
(or arXiv:2605.23994v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.23994
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.2312/egs.20261006
DOI(s) linking to related resources
Submission history
From: Jack Parry [view email] [v1] Sun, 17 May 2026 23:01:39 UTC (9,873 KB)
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