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Rewriting Video: Text-Driven Reauthoring of Video Footage

Sitong Wang, Anh Truong, Lydia B. Chilton, Dingzeyu Li · Jan 13, 2026 · Citations: 0

Automatic Metrics
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging. Even simple edits often demand expertise, time, and careful planning, constraining how creators envision and shape their narratives. Recent advances in generative AI suggest a new paradigm: what if editing a video were as straightforward as rewriting text? To investigate this, we present a tech probe and a study on text-driven video reauthoring. Our approach involves two technical contributions: (1) a generative reconstruction algorithm that reverse-engineers video into an editable text prompt, and (2) an interactive probe, Rewrite Kit, that allows creators to manipulate these prompts. A technical evaluation of the algorithm reveals a critical human-AI perceptual gap. A probe study with 12 creators surfaced novel use cases such as virtual reshooting, synthetic continuity, and aesthetic restyling. It also highlighted key tensions around coherence, control, and creative alignment in this new paradigm. Our work contributes empirical insights into the opportunities and challenges of text-driven video reauthoring, offering design implications for future co-creative video tools.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging."

Reported Metrics

partial

Coherence

Useful for evaluation criteria comparison.

"It also highlighted key tensions around coherence, control, and creative alignment in this new paradigm."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Even simple edits often demand expertise, time, and careful planning, constraining how creators envision and shape their narratives."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

coherence

Research Brief

Metadata summary

Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging.
  • Even simple edits often demand expertise, time, and careful planning, constraining how creators envision and shape their narratives.
  • Recent advances in generative AI suggest a new paradigm: what if editing a video were as straightforward as rewriting text?

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • To investigate this, we present a tech probe and a study on text-driven video reauthoring.
  • A technical evaluation of the algorithm reveals a critical human-AI perceptual gap.

Why It Matters For Eval

  • A technical evaluation of the algorithm reveals a critical human-AI perceptual gap.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: coherence

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Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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