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    <responseDate>2026-10-09T20:43:16Z</responseDate>
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    <identifier>10.57760/sciencedb.013zw</identifier>
    <datestamp>2026-09-29T16:16:33Z</datestamp>
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  <dc:date>2026-09-29</dc:date>
  <dc:title>Supplementary Dataset for &amp;quot;From Shooting to Generating: Interaction Paradigms and a Methodological Framework for AIGC Live‑Action Film Creation&amp;quot;</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.013zw</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This is the supplementary archived dataset for the paper From Shooting to Generating: Interaction Paradigms and a Methodological Framework for AIGC Live‑Action Film Creation. Bilingual Chinese‑English Excel files are provided. The dataset consists of nine worksheet modules: ① statistics of generative‑AI tool adoption from MIT AI Film Hack (2023‑2025); ② creators&amp;rsquo; importance ratings for key creative operations; ③ multi‑dimensional comparison of mainstream global AIGC text‑to‑video platforms, including pricing, maximum duration, resolution and positioning (data snapshot as of 2026‑09‑25); ④ stage‑by‑stage comparison between traditional film workflow and AIGC‑driven film workflow; ⑤ comparison matrix of five human‑AI co‑creation interaction paradigms; ⑥ theoretical framework for the previz‑to‑final workflow based on white‑model rendering; ⑦ AIGC live‑action‑film creator competence model with illustrative radar values; ⑧ full reference metadata including authors, titles, venues, DOI or arXiv identifiers; ⑨ real‑world industrial production cases cited in the manuscript with provenance information.Two data tags are applied: [E] denotes empirical data collected from published literature, official product pages and public industry surveys; [A] represents the author&amp;rsquo;s conceptual analytical frameworks rather than measured experimental data. Commercial platform parameters change frequently, and re‑verification against official sources is required before formal usage. No human‑subject data or personal private information was collected in this research. This dataset supports reproducibility and comparative analysis for further studies on AIGC live‑action film, human‑AI co‑creation, generative video and interaction design.</dc:description>
  <dc:subject>AIGC Live‑Action Film; Text‑to‑Video Model; Human‑AI Co‑Creation; Interaction Paradigm; Film Creation Methodology</dc:subject>
  <dc:creator>Abuduaini Tuoheti</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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