Design and Research of an Intelligent Houdini Model Library Based on Procedural Modeling Technology
DOI: https://doi.org/10.62517/jike.202604102
Author(s)
Haolong Yang, Jiayi Shen, Ning Mao, Yicong Lin, Guwei Li*
Affiliation(s)
Artificial Intelligence College, Zhejiang Dongfang Polytechnic, Wenzhou, Zhejiang, China
*Corresponding Author
Abstract
Aiming at the problems of low efficiency, poor reusability and difficulty in dynamic adjustment of traditional 3D modeling, this paper proposes a design scheme of an intelligent Houdini model library based on Procedural Content Generation (PCG) technology. This scheme integrates Houdini's node-based workflow, VEX programming language and AI-assisted generation mechanism to construct an intelligent model library system that supports parametric control, logical combination and automatic error correction. By parsing the model generation rules in fields such as architecture and games, basic components are encapsulated as procedural assets, realizing the rapid generation and dynamic iteration of complex scenes. Experiments show that this model library can shorten the production cycle of similar models by more than 60%, and has good scalability and fault tolerance, providing an efficient solution for large-scale digital content production.
Keywords
Procedural Modeling; Houdini; Parametric Design; Digital Content Generation
References
[1] Meta Reality Labs. “Varifocal Display Latency Optimization.” SID Symposium Digest, 54(1), 112-115, 2023.
[2] DeepMind. “Reinforcement Learning for Adaptive Fire Scenarios.” Nature Machine Intelligence, 6(7), 732-744, 2022.
[3] Yang Dai, Zhiyuan Luo. “Review of Unsupervised Person Re-Identification.” Journal of New Media, 3(4), 129-136, 2021.
[4] Meskat Jahan, Manajir Hassan, Sahadat Hossin, et al. “Unsupervised person Re-identification: A review of recent works,” Neurocomputing, 572(1), 127193–127196, 2024.
[5] Zhenping Xia, Qishuai Han, Yuning Zhang, et al. “Objective quantification of dynamic spatial distortions for enhanced realism in virtual environments.” Virtual Reality, 29(1), 39-39, 2025.
[6] Mohammad BaniSalman, Mohammad Aljaidi, Najat Elgeberi, et al. “VRDeepSafety: A Scalable VR Simulation Platform with V2X Communication for Enhanced Accident Prediction in Autonomous Vehicles.” World Electric Vehicle Journal, 16(2), 82-82, 2025.
[7] R. Wazirali, “Aligning education with vision 2030 using augmented reality,” Computer Systems Science and Engineering, 36(2), 342–351, 2021.
[8] Menzemer Leo Willem, Ronchi Enrico, Karsten Mette Marie Vad, et al. “A scoping review and bibliometric analysis of methods for fire evacuation training in buildings.” Fire Safety Journal, 136(1), 15–17, 2023.
[9] Ali Haider, Ilya Morev, Aleksi Rintanen, et al. “Accelerated numerical simulations of hydrogen flames: Open-source implementation of an advanced diffusion model library in OpenFOAM,” International Journal of Hydrogen Energy, 189(1), 152115-152115, 2025.
[10]Zekai Liu, Genming Lai, Yunxing Zuo, et al. “AI-driven next-generation lithium-ion battery design automation (BDA) software,” National Science Open, 4(6), 10–15, 2025.