[Submitted on 15 Jul 2026]
Title:Inference-Time Concept Suppression and Video-Centric Evaluation for Text-to-Video Models
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Abstract:Text-to-video (T2V) generators can synthesize realistic and temporally coherent videos, but controllably removing a target concept from a generator remains difficult.
Unlike text-to-image concept erasure, T2V unlearning must suppress a target concept that may persist across frames while preserving non-target subjects, actions, scenes, and temporal structure.
We propose \textbf{SIRUS}, a training-free inference-time framework for concept-level T2V unlearning.
Given textual aliases of a target concept, SIRUS localizes target-related prompt evidence and suppresses target expression during sampling, without updating the text encoder or denoising network.
We further introduce a video-oriented evaluation framework for T2V unlearning that separately measures target forgetting, non-target preservation, video quality, jailbreak robustness, and efficiency, using video-level failure criteria, frame-level residue statistics, paired preservation analysis, VBench-based quality diagnostics, and deployment overhead measurement.
Across five safety, object, and style concepts on CogVideoX, SIRUS reaches 70.4\% average forgetting success and 25.7\% average frame hit, compared with 44.4\% / 47.2\% for VideoEraser, while reducing the average VBench quality drop from -0.043 to -0.016, yielding the strongest forgetting-quality trade-off among fully evaluated baselines.
Transfer experiments on Wan2.2 further suggest that SIRUS generalizes across modern T2V backbones.
Comments: 29 pages, 9 figures, 11 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.14194 [cs.CV]
(or arXiv:2607.14194v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.14194
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Wenxuan Chen [view email] [v1] Wed, 15 Jul 2026 16:26:09 UTC (12,633 KB)
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