Dataset for: Generative AI Adoption in 3D Animation Production: A Pipeline-Wide Empirical Benchmark of Practitioner Perceptions and the HGAI Hybrid Pipeline Framework

This dataset contains the high-resolution figures, renders, and visual assets associated with the research paper titled: "Generative AI in 3D Animation: A Mixed-Method Empirical Analysis of Adoption, Reliability, and Hybrid Pipeline Frameworks." Overview: The included files provide visual evidence and framework diagrams supporting a cross-sectional mixed-method study ($N = 126$) that investigates the integration of Generative AI (GenAI) across the 3D animation pipeline. The research identifies a significant adoption gap (34.9%) between pre-production and technical stages and introduces the Human-Guided AI (HGAI) Hybrid Pipeline Framework. Contents: The uploaded ZIP archive includes: High-Resolution Framework Diagrams: Detailed visualizations of the proposed HGAI Hybrid Pipeline (AI-Led, Hybrid, and Human-Led phases). Comparative Renders: Visual samples illustrating the current capabilities and limitations of GenAI in technical 3D stages (e.g., denoising and rigging assistance). Data Visualizations: Charts and graphs representing the statistical differences in adoption intensity between students and industry professionals. Usage Note: All image files are named according to their corresponding figure numbers in the manuscript for ease of reference. These materials are intended to support the reproducibility of the study and provide clarity on the qualitative visual analysis performed. A text file inside rar file is also provided to explain which image referes to what.