AIGC-Driven Film Rendering Workflow Based on Tencent Hunyuan 3D: Construction and Validation

Abstract

Traditional film and television 3D production suffers from prominent practical pain points including repetitive manual modeling & topology correction, frequent cross-tool format conversion leading to fragmented workflows, long production cycles for high-precision assets, and unstable rendering quality for dynamic camera shots caused by manual operational errors. To address these industry challenges, this study develops an integrated AIGC-based rendering workflow utilizing Tencent's Hunyuan 3D platform. Thirty professionals from the film and television sector participated in a comparative experiment comparing traditional methods with the AI-driven workflow, with comprehensive evaluation conducted through objective time measurement, a five-point Likert scale, and questionnaires. Results demonstrate that the AIGC workflow requires an average of 16–20 minutes per single 3D film and television asset, significantly improving production efficiency; AI-generated 3D assets achieve full compatibility with the tested mainstream industry tools and renderers, and user satisfaction scores reached 4.26 out of 5. This solution effectively resolves common issues such as model topology errors, material texture inconsistencies, and cumbersome rendering debugging processes, exhibiting strong versatility and practical applicability. The research establishes a standardized, replicable intelligent production framework that provides actionable guidance for advancing smart transformation in 3D content creation within the film and television industry.