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MFVSR: A Multi-Style and Fine-Grained Benchmark for Video Subtitle Removal

Xiyan Huang, Qingdong He, Peng Tang, Bo Yin, Chaoyi Wang, Xiaobin Hu

arXiv:2610.18461v1cs.CV

TL;DR

Existing VSR resources lack unified paired supervision, controlled subtitle variation, and evaluation tailored to blind and mask-guided removal and localized errors. MFVSR combines those elements in a benchmark, and experiments reveal distinct quality and reliability trade-offs across methods, with stronger human-study agreement for spatial than temporal components.

  • Problem

    Existing VSR resources are fragmented, insufficiently structured by subtitle conditions, and lack unified evaluation for blind and mask-guided removal, spatially localized performance, coverage, and failures.

    Source: §1 · pp. 1–2

  • Method

    MFVSR combines paired videos and controlled multilingual subtitles with separate blind and mask-guided evaluation across spatial views, fidelity dimensions, coverage, and failures.

    Source: §1 · p. 2, §3.1 · p. 7, §4 · p. 9

  • Results

    Experiments on six representative methods reveal quality trade-offs and gaps between conditional performance and complete-video delivery; held-out human comparisons support spatial components more strongly than temporal ones.

    Source: §1 · p. 2, §4 · p. 10

  • Takeaways & Limitations

    MFVSR provides a diagnostic basis for comparing VSR systems and supporting reproducible research and future development.

    Source: §4 · p. 9

    The auxiliary collection has no clean targets and is excluded from paired quantitative evaluation; no additional privacy screening or de-identification was applied.

    Source: ETHICS STATEMENT · p. 10

Abstract

from arXiv · show

Video subtitle removal (VSR) is an important video restoration task, yet existing evaluation remains limited by low-resolution datasets, fragmented test sets, and generic video-inpainting protocols that fail to adequately capture localized restoration errors, unintended modifications, temporal instability, and incomplete outputs. In this paper, we introduce MFVSR, a large-scale multi-style benchmark for fine-grained and reliability-aware video subtitle removal evaluation. MFVSR distinguishes itself through three key features: (1) Large-scale and diverse data, comprising 3,000 paired videos from Real-HQ, Real-LQ, and AI-Generated sources, with an auxiliary set of 953 in-the-wild real videos for real-world coverage analysis; (2) Controlled subtitle diversity, spanning 16 languages and four factors covering spatial layout, temporal behavior, visual style, and language mode; (3) Fine-grained evaluation, introducing an information-conditioned, spatially decoupled protocol that separates blind from mask-guided removal and measures Restoration Fidelity, Preservation Fidelity, Temporal Fidelity, coverage, and failures. Experiments on six representative methods reveal substantial discrepancies between local restoration, outside-region preservation, temporal consistency, and full-set reliability that are obscured by conventional full-frame metrics. MFVSR therefore provides a more comprehensive and diagnostic foundation for evaluating and advancing video subtitle removal methods.

1 INTRODUCTION

Video subtitle removal requires localization, background reconstruction, preservation of unaffected regions, and temporal consistency, but existing resources do not support systematic, unified evaluation. MFVSR combines paired data, controlled subtitle diversity, real-world coverage, and fine-grained evaluation; six-method results reveal trade-offs that full-frame similarity misses.

1 INTRODUCTION

  • Existing resources are fragmented, weakly structured by subtitle conditions, and lack subtitle-specific evaluation of blind versus mask-guided removal, spatial regions, coverage, and failures.Current resources include short 128 × 128 clips or paper-specific test sets, limiting broad and reproducible comparison.Source: §1 · pp. 1–2
  • MFVSR provides 3,000 paired videos across three source strata, 953 auxiliary in-the-wild videos, 16 languages, and 24 controlled subtitle configurations.The benchmark supplies clean targets, glyph masks, BBoxes, model-input masks, and rendering metadata; its protocol separates blind from mask-guided removal and reports five spatial views, three fidelity dimensions, coverage, and failures.Source: §1 · p. 2, Figure 1 · p. 2
  • Six representative methods show trade-offs across restoration, preservation, temporal fidelity, and reliability that conventional full-frame similarity does not capture.Held-out human comparisons support the spatial components more strongly than the temporal component and do not establish an SR-Q advantage over conventional Overall metrics.Source: §1 · p. 2
  • MFVSR is intended as a diagnostic basis for comparing VSR systems, with benchmark resources planned for public release to support reproducible research.The release plan is subject to the stated availability of the benchmark resources.Source: §1 · p. 3

2 MFVSR BENCHMARK CONSTRUCTION

MFVSR constructs paired subtitle-removal data from filtered real and generated videos, renders subtitles across independently configurable factors, and prepares aligned supervision assets. It routes models according to their input setting and reports broad video and subtitle coverage alongside benchmark-resource comparisons.

2.1 MULTI-SOURCE COLLECTION AND BASE-VIDEO FILTERING

  • The 3,000 clean base videos are split equally into Real-HQ, Real-LQ, and AI-Generated strata; automatic screening and manual verification remove unsuitable overlays and content.Real-HQ and Real-LQ are resolution-based strata, not perceptual-quality labels, and the three sources do not imply exhaustive content coverage.Source: §2.1 · p. 3

2.2 CONTROLLABLE SUBTITLE RENDERING ENGINE

  • Subtitle rendering factorizes spatial layout, temporal behavior, visual style, and language mode into 24 major configuration cells, with finer subtypes adding within-cell variation.Spatial layout and temporal behavior are orthogonal, while configured factor independence does not imply statistically independent effects on removal difficulty.Source: §2.2 · p. 4

2.3 PREPROCESSING AND PAIRED-ASSET GENERATION

  • Each clean base yields a subtitled video, pixel-level glyph-mask video, and per-frame BBox JSON; the glyph mask supports evaluation while BBox-derived masks serve as model inputs.The mask formats encode the same spatial and temporal information; they are not separate ground-truth annotations.Source: §2.3 · pp. 4–5

2.4 EXECUTION ROUTING

  • Blind methods receive only the corrupted video, whereas mask-guided methods also receive the canonical BBox-derived mask, with the two families ranked separately.Model-specific preprocessing follows evaluated inference configurations; routing does not access the glyph mask or clean target.Source: §2.4 · p. 5

2.5 STATISTICS AND ANALYSIS

  • MFVSR covers 16 languages and 24 subtitle configurations with 123–126 videos each, alongside varied resolutions, sequence lengths, durations, and content categories.The resource comparison describes MFVSR as combining 3,000 paired clips and controlled strata with separate input-setting reports, spatial and temporal evaluation, and execution-failure and valid-coverage statistics.Source: §2.5 · p. 5, Table 2 · p. 6

3 EXPERIMENTS

MFVSR’s experiments use controlled inputs and spatially decoupled metrics to compare six methods across restoration, preservation, temporal fidelity, and delivery. Results show that these capabilities and delivery regimes differ, so full-frame scores or evaluability alone cannot summarize performance.

3.1 EXPERIMENTAL SETTING

  • MFVSR renders temporally controlled subtitles over clean targets to produce corrupted videos, glyph masks, frame-level BBoxes, and rendering attributes.The attributes encode subtitle conditions, and the six evaluated methods comprise one blind method and five mask-guided methods.Source: §3.1 · p. 6
  • Evaluation separates Restoration Fidelity, Preservation Fidelity, and Temporal Fidelity across five spatial views, with Audit300 providing detailed metrics on a fixed 300-video subset.Blind and mask-guided methods are reported separately because their inputs differ; quality estimates use video-level macro means and bootstrap 95% confidence intervals.Source: §3.1 · p. 7

3.2 OVERALL RESULTS

  • The strongest methods differ across Restoration, Preservation, and Temporal fidelity on shared Audit300 videos, so gains in one capability do not imply gains in another.DiffuEraser favors glyph-core reconstruction, ProPainter Outside preservation and temporal consistency, and MiniMax-Remover Outside TWE-X; EffectErase’s favorable conditional values apply only to its verified prefix.Source: §3.2 · p. 8

3.3 FINDINGS OF MFVSR

  • Subtitle and Outside LPIPS disagree on five of ten mask-guided method pairs, while BBox TWE-Excess and BBox TC disagree on six of ten.Overall LPIPS follows Outside LPIPS more closely than Subtitle LPIPS, supporting separate reporting of Restoration, Preservation, and Temporal Fidelity.Source: §3.3 · p. 8, Figure 5 · p. 8
  • Real-HQ has higher Subtitle and Outside LPIPS than AI-Generated videos for all six methods, yet lower BBox TC for five methods.Source and resolution are coupled in Audit300, so this contrast describes joint data strata rather than an isolated resolution effect; duration and style also shift errors by method.Source: §3.3 · pp. 8–9, Figure 6 · p. 9
  • Complete-video rates range from 3.9% to 98.3% across 3,000 scheduled videos, although every method produces evaluable output for more than 95% of instances.High temporal coverage also does not guarantee complete-video delivery, so delivery and quality should be reported separately.Source: §3.3 · p. 9, Table 5 · p. 9
  • Extending evaluation from the common intersection to native evaluable sets changes assessed scope and quality distributions in method- and metric-dependent ways.Typical and tail errors may shift differently, and spatial and temporal quality can move in opposite directions; native-set comparisons are not controlled cross-method rankings.Source: §3.3 · p. 9
  • Human–metric agreement is stronger for Restoration and Preservation than for Temporal fidelity, while SR-Q shows no consistent advantage over preregistered Overall-region baselines.The study retains the three-dimensional raw profile as its primary diagnostic representation and uses SR-Q only as a secondary summary.Source: §3.3 · p. 9

4 CONCLUSION

The conclusion presents MFVSR as a benchmark for systematic VSR evaluation and summarizes its controlled data and separate evaluation dimensions. Experiments expose quality–delivery gaps, while human validation is stronger for spatial than temporal components.

4 CONCLUSION

  • MFVSR combines paired videos, controlled multilingual subtitle variations, and an auxiliary collection of naturally subtitled videos.Its protocol separates blind from mask-guided removal and evaluates restoration, preservation, temporal fidelity, and delivery coverage.Source: §4 · p. 9
  • Experiments on six representative methods reveal quality trade-offs and gaps between conditional performance and complete-video delivery.The held-out human study shows stronger agreement for spatial than temporal components, leaving temporal perceptual validation open.Source: §4 · p. 10
  • The authors state that MFVSR and its analyses are intended to support reproducible comparison and development of more reliable video subtitle removal methods.Source: §4 · p. 10

AI USE STATEMENT

The authors report that generative AI tools assisted with implementation, evaluation-protocol review, manuscript organization, and language editing; they reviewed the text and verified results and code.

AI USE STATEMENT

  • Generative AI assisted with code implementation and debugging, protocol review, manuscript organization, and language editing; the authors reviewed the text and verified results and evaluation code.Source: AI USE STATEMENT · p. 10

ETHICS STATEMENT

The auxiliary naturally subtitled collection has no clean targets and is excluded from paired quantitative evaluation; no additional privacy screening or de-identification was applied.

ETHICS STATEMENT

  • The 953 naturally subtitled videos have no clean targets and are excluded from paired quantitative evaluation.Source: ETHICS STATEMENT · p. 10
  • No additional privacy screening or de-identification was applied to the auxiliary collection.Source: ETHICS STATEMENT · p. 10

REPRODUCIBILITY STATEMENT

MFVSR’s reproducibility materials cover benchmark construction, evaluation design, and implementation details, with appendices documenting validation, metrics, supplementary results, and qualitative cases. Related work motivates a dedicated subtitle benchmark and separates blind localization from mask-guided restoration.

REPRODUCIBILITY STATEMENT

  • MFVSR documents evaluation design, frozen identifiers, model configurations, code, and provenance records, subject to upstream licensing constraints.The inference logs cover 10,137.4 GPU-hours across six baselines.Source: REPRODUCIBILITY STATEMENT · p. 10

APPENDIX CONTENTS

  • Appendices cover benchmark and evaluation details, supplementary results, computational workload, and qualitative restoration, preservation, and temporal-fidelity cases.The listed evaluation details include human evaluation and SR-Q validation scope.Source: APPENDIX CONTENTS · p. 14

A RELATED WORK

  • MFVSR separates blind methods, which infer where and how to edit, from mask-guided methods, which receive spatial side information.Combining the settings in one ranking would conflate localization with restoration.Source: A RELATED WORK · p. 14
  • Video-inpainting and object-removal methods provide relevant mask-guided baselines, but are not subtitle localizers or evidence of blind subtitle-removal ability.Their released interfaces can use the benchmark’s canonical BBox-derived subtitle annotation.Source: A RELATED WORK · p. 14
  • Prior diagnostic benchmarks motivate controlled analysis, but their corruptions do not target multilingual subtitle typography, motion, animation, or language layout.MFVSR retains released metric definitions and weights where possible, changing spatial support or controlled input for regional diagnosis.Source: A RELATED WORK · p. 15

B.1 DATASET VALIDATION

The evaluation workflow validates video, target, mask, annotation, and output correspondence, then applies frozen sampling and spatial supports. Regional and temporal metrics use specified compatibility choices to diagnose distinct benchmark views.

B.1 DATASET VALIDATION

  • Samples are rejected when glyph pixels fall outside rasterized BBox unions, masks fail to span the target timeline, or frame rates mismatch by more than 0.01 fps.Glyph-core validation does not require shadows, glow, outlines, or subtitle boxes to fit inside the BBox union.Source: §B.1 · p. 15
  • Prediction lengths follow each method’s temporal contract, with verified prefixes recorded as partial outputs rather than padded.Resolution changes are inverted using frozen transforms, and correspondence is restricted to verified indices.Source: §B.1 · p. 15, §B.2 · p. 16

B.2 EVALUATION SUPPORTS, SAMPLING, AND AGGREGATION

  • Lite3000 samples at most 48 spatial frames and 32 adjacent pairs; Audit300 reuses that support and adds structural and temporal metrics.Selected indices depend only on annotations and video length.Source: §B.2 · p. 16
  • Audit300 selects 300 videos from the common-evaluable pool using fixed-seed metadata optimization, without metrics, method identities, ranks, or human judgments.Selection reduces composition deficits across sources, resolutions, duration, style complexity, and subtitle factors.Source: §B.2 · p. 16
  • Results use video-level macro means and fixed-seed bootstrap intervals, while reliability covers the scheduled set and conditional quality covers each method’s evaluable set.Blind and guided methods are reported separately, and quality is undefined for failed outputs.Source: §B.2 · p. 16

B.3 BENCHMARK-SPECIFIC METRIC CONVENTIONS

  • Regional PSNR, SSIM, and LPIPS use specified pooling, boundary, and fractional-mask conventions, while Overall SSIM and LPIPS retain their standard scalars.Sparse-glyph SSIM is diagnostic; regional LPIPS uses AlexNet v0.1.Source: §B.3 · p. 17
  • Conditioned DISTS replaces a selected target region with the prediction, and temporal diagnostics use clean-target flow plus a flow-free measure that penalizes suppression of legitimate target changes.Conditioned DISTS is not presented as an official dense DISTS variant; negative signed target-baseline differences do not establish superior fidelity.Source: §B.3 · p. 17

B.4 HUMAN-EVALUATION PROTOCOL

The held-out study compares anonymized predictions against synchronized clean references across four tasks, using verified shared support and video pairs as the independent unit. SR-Q is retained for reproducibility but excluded from primary analyses after showing no advantage over conventional Overall metrics.

B.4 HUMAN-EVALUATION PROTOCOL

  • The held-out study covers 100 distinct videos and all 15 method pairs, using each pair’s shorter verified prefix and intersected valid spatial support.The videos are disjoint from Audit300 and the pilot; verified partial outputs remain eligible.Source: §B.4 · pp. 17–18
  • Four tasks assess restoration, preservation, temporal fidelity, and integrated preference through synchronized predictions and a clean reference.Preservation masks the operational BBox in all views to isolate Outside content; responses unlock after 90% of each required view is watched.Source: §B.4 · p. 18, Figure 7 · p. 18
  • After quality control, 16 annotators provide 944 valid judgments, with unique pluralities determining labels and exact ties remaining undecided.The video pair is the independent unit of analysis.Source: §B.4 · p. 18

B.5 SR-Q: DEFINITION AND VALIDATION SCOPE

  • SR-Q showed no held-out advantage over conventional Overall metrics and is excluded from the primary leaderboard and stratified analysis.Its definition is retained to make the comparison reproducible.Source: §B.5 · p. 18
  • SR-Q normalizes metric values using frozen P5/P95 anchors from the six-model Audit300 grid, without human labels or refitting for new methods.The component profile averages normalized Restoration, Preservation, and regional Temporal scores.Source: §B.5 · p. 18
  • SR-Q is not extrapolated to failed or unaudited outputs, multiplied by coverage, or combined with computational cost.Components are not imputed when support is missing.Source: §B.5 · p. 19

C.1 COMPLETE AUDIT300 RESULTS

Audit300 comparisons show that regional spatial and temporal metrics can order methods differently, supporting separate fidelity reporting rather than reliance on a single full-frame score. These results are conditional on verified method outputs.

C.1 COMPLETE AUDIT300 RESULTS

  • Audit300 spatial and temporal values are conditional on each method’s verified output, with BBox TWE-Excess using symmetric support from 299 videos.Other reported entries use 300 videos and include video-bootstrap 95% intervals.Source: §C.1 · p. 19, Table 9 · p. 19, Table 10 · p. 19

C.2 DECOUPLED QUALITY

  • Subtitle and Outside LPIPS disagree on five of ten mask-guided method pairs, while BBox TWE-Excess and BBox TC disagree on six of ten.Overall LPIPS tracks Outside LPIPS more closely than Subtitle LPIPS; the comparisons include EffectErase’s verified-prefix results.Source: §C.2 · pp. 19–20, Table 11 · p. 20
  • EffectErase has the lowest Subtitle LPIPS among guided methods, whereas ProPainter has the lowest Outside LPIPS, yielding ranking reversals.Removing EffectErase still leaves spatial and temporal ranking reversals, so its short outputs cannot fully explain the disagreement.Source: §C.2 · p. 19
  • Low TWE-Excess can coexist with a mismatch in target changes measured by TC, so the two temporal metrics capture different behavior.The results are descriptive because temporal support is not aligned in the sensitivity check.Source: §C.2 · p. 20

C.3 DATASET AND SUBTITLE DIAGNOSTICS

The diagnostics show that benchmark outcomes vary with video slices and subtitle conditions, while delivery, evaluable scope, and human alignment impose distinct limits on interpreting quality comparisons.

C.3 DATASET AND SUBTITLE DIAGNOSTICS

  • Real-HQ has the highest Subtitle LPIPS for every method, and Medium clips exceed Short clips for guided methods, but source and resolution are confounded.The Hard style-complexity group is easier for guided methods on Subtitle LPIPS but harder for CLEAR, so the score is not a universal difficulty scale.Source: §C.3 · p. 20
  • Subtitle-factor effects are method-dependent: bilingual subtitles raise Subtitle LPIPS across methods, while dynamic and decorated styles yield differing responses; text-tight BBoxes also limit Outside fidelity for Boxed subtitles.The Boxed/Subtitle-box stress set omits Preservation and BBox temporal scores because its BBoxes need not cover the rendered box.Source: §C.3 · p. 20

C.4 FULL-SET DELIVERY AND COVERAGE

  • ProPainter usually completes videos but loses coverage on long, high-resolution inputs, while CLEAR and EffectErase predominantly return partial outputs under frozen temporal contracts.The strict six-method complete non-box intersection contains only 80 short, 720p-class videos, nearly all AI-generated, and is not representative for full-video efficiency comparisons.Source: §C.4 · pp. 20, 22

C.5 EVALUABLE SCOPE AND CONDITIONAL QUALITY

  • Expanded evaluable scopes differ in composition, and method-specific quality shifts show that a lower median or temporal discrepancy can coincide with deterioration elsewhere.DiffuEraser’s median Subtitle LPIPS rises from 0.228 to 0.285 while median BBox TC falls from 0.0212 to 0.0187; MiniMax-Remover’s median Subtitle LPIPS falls from 0.253 to 0.219 while its 90th percentile rises from 0.558 to 0.671.Source: §C.5 · p. 23

C.6 HUMAN ALIGNMENT AND QUALITATIVE EVIDENCE

  • Human validation supports Restoration and Preservation regional views more clearly than combining their metrics, and neither component improves observed accuracy over its LPIPS counterpart.The Restoration and Preservation components agree with most decisive preferences.Source: §C.6 · p. 23
  • Temporal human judgments remain uncertain because most pairs are undecided, inter-annotator agreement is low, and the decisive subset yields wide intervals.The SR-Q summary also fails its preregistered promotion rule: on decisive Overall pairs it does not outperform the strongest conventional candidate.Source: §C.6 · p. 23

D COMPUTATIONAL WORKLOAD

The appendix reports inference workload alongside diagnostic case comparisons. The examples separate restoration, preservation, and temporal fidelity, while noting the limits of selected cases and short temporal strips.

D COMPUTATIONAL WORKLOAD

  • Recorded inference workload totals 10,137.4 GPU-hours across 10,137.4?Table 16 covers 17,814 of 18,000 scheduled records; totals include timed failures and observed runtime to termination for partial outputs.Source: D COMPUTATIONAL WORKLOAD · p. 25, Table 16 · p. 25

E QUALITATIVE CASES AND DIAGNOSTIC INSIGHTS

  • The five cases per diagnostic are selected by six-model score range, so the gallery illustrates disagreements rather than failure frequencies or an unbiased ranking.Selection uses neither SR-Q nor human labels; restoration views use local crops, preservation views retain full-frame context, and temporal strips show three shared consecutive frames.Source: E QUALITATIVE CASES AND DIAGNOSTIC… · p. 26

E.1 RESTORATION: TEXT REMOVAL AND SCENE RECONSTRUCTION

  • In restoration cases, visible text remnants, smooth bands, and altered scene structure show that removing glyphs does not ensure the replacement matches the clean scene.Examples include altered cloud structure, stage geometry, transparent glass, natural detail, and localized objects.Source: §E.1 · p. 26, RESTORATION ACROSS BACKGROUNDS · p. 27

E.2 PRESERVATION: CHANGES BEYOND THE SUBTITLE

  • Full-frame preservation comparisons expose color, boundary, and texture changes beyond the subtitle region that local crops can miss.Outside fidelity records these differences independently of the repair itself; the separate box-style stress test addresses effects beyond the operational BBox.Source: §E.2 · p. 28, PRESERVATION UNDER MOVING AND ANIMATED… · p. 29

E.3 TEMPORAL FIDELITY: CHANGES RELATIVE TO THE TARGET

  • Stable-looking subtitle remnants can still differ from the clean target, and three-frame strips cannot establish perceptible flicker over a full video.Temporal fidelity must account for target changes as well as output stability; the held-out temporal study also has a high undecided rate.Source: §E.3 · p. 30, TEMPORAL FIDELITY UNDER SUBTITLE MOTION · p. 31

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