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<p>Generative AI (GenAI) is rapidly transforming 3D animation and multimedia production, yet empirical data on adoption patterns across the full pipeline, early-career practitioner-rated reliability, and student–professional differences remain scarce. This cross-sectional mixed-method study (<i>N</i> = 126) surveys students, educators, and early-career industry practitioners. Adoption is highest in pre-production (storyboarding 45.6%; concept art 41.6%) and lowest in technical stages (rigging assistance 16.0%; denoising 10.4%), a gap of 35.2% points. The mean perceived reliability of current GenAI tools is 2.95/5 (SD = 1.07), indicating experimental rather than production-ready status. Students and professionals do not differ significantly in perceived reliability (students/educator: M = 2.88, SD = 1.13; professionals: M = 3.06, SD = 0.95; Welch t(112) = − 0.96, <i>p</i> = 0.341); however, professionals demonstrate significantly higher adoption intensity (Mann–Whitney U = 1427, <i>p</i> = 0.024). The dominant integration barrier is output inconsistency (46.4%). The Human-Guided AI (HGAI) Hybrid Pipeline Framework is introduced as a descriptive, data-calibrated, three-phase integration model: AI-Led for pre-production (adoption ≥ 40%), Hybrid for mid-pipeline (20–39%), and Human-Led for technical stages (&lt; 20% and reliability &lt; 3.0/5). Limitations include a single-item reliability measure, India-weighted sample, and cross-sectional design. Replication with globally distributed, professionally weighted samples is required.</p>

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Generative AI adoption in 3D animation production: a pipeline-wide empirical benchmark of early-career practitioner perceptions and the HGAI hybrid pipeline framework

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Sandeep Maithani

摘要

Generative AI (GenAI) is rapidly transforming 3D animation and multimedia production, yet empirical data on adoption patterns across the full pipeline, early-career practitioner-rated reliability, and student–professional differences remain scarce. This cross-sectional mixed-method study (N = 126) surveys students, educators, and early-career industry practitioners. Adoption is highest in pre-production (storyboarding 45.6%; concept art 41.6%) and lowest in technical stages (rigging assistance 16.0%; denoising 10.4%), a gap of 35.2% points. The mean perceived reliability of current GenAI tools is 2.95/5 (SD = 1.07), indicating experimental rather than production-ready status. Students and professionals do not differ significantly in perceived reliability (students/educator: M = 2.88, SD = 1.13; professionals: M = 3.06, SD = 0.95; Welch t(112) = − 0.96, p = 0.341); however, professionals demonstrate significantly higher adoption intensity (Mann–Whitney U = 1427, p = 0.024). The dominant integration barrier is output inconsistency (46.4%). The Human-Guided AI (HGAI) Hybrid Pipeline Framework is introduced as a descriptive, data-calibrated, three-phase integration model: AI-Led for pre-production (adoption ≥ 40%), Hybrid for mid-pipeline (20–39%), and Human-Led for technical stages (< 20% and reliability < 3.0/5). Limitations include a single-item reliability measure, India-weighted sample, and cross-sectional design. Replication with globally distributed, professionally weighted samples is required.