Paper page - DeOverlay: A Benchmark and Source-Anchored Flow for Video Overlay Removal

Source: https://huggingface.co/papers/2610.26081

Abstract

DeOverlay uses source-anchored flow matching and visibility-normalized temporal aggregation to remove video overlays while preserving scene structure, and introduces DVTE-Bench for standardized evaluation.

Video overlay removal aims to recover scenes obscured by subtitles, titles, and logos while preserving surrounding content. Existing methods use temporal propagation or source-conditioned generation, but can leave residual overlays and distort scene structure when useful source information is obscured. We propose DeOverlay, a source-anchored flow model that reconstructs the clean video from the source latent with fixed source guidance. Specifically, we train a sharedvelocity networkalong paired source-to-clean paths usingregion-weighted flow matching, enabling successive cor- rections from intermediate states. To select local source cues, we developvisibility-normalized temporal aggregationthat excludes masked positions and averages the remaining observations. Global source context and edit-region embeddings complement these cues through additive condi- tioning, retaining a singlevideo-token sequence. To evaluate scene recovery across diverse overlay conditions, we introduceDVTE-Bench, comprising 1,631 paired videos across eight text-overlay categories, with OCR-screened clean targets, frame-aligned pixel masks, and a common evaluation protocol. Experiments on the complete release show that DeOverlay outperforms four baselines on all five reconstruction and perceptual metrics, reducingMask LPIPSandMask VFIDby 58.3% and 70.3%, respectively, relative to the strongest evaluated baseline. Additional qualitative examples demonstrate the removal of graphic logos and stickers. We will make the complete benchmark, evaluation code, and test outputs availabl. Project page:https://yahooo-m.github.io/DVTE-Bench/

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