Гібридні моделі синтезу складних 3d-середовищ на основі нейро-процедурних підходів

Abstract

This paper proposes a hybrid approach to 3D content generation by integrating formal procedural rules with deep learning techniques. The methodology employs shape grammars and production systems to ensure structural integrity, while neural frameworks such as PyTorch and the Blender API enhance visual realism and detail. The optimization process uses a synergistic approach in which evolutionary algorithms perform global parameter searches, while reinforcement learning provides fine-tuning for specific environmental constraints. The proposed method significantly reduces time and resource costs in the production of digital assets. Practical applications include generating synthetic datasets for robotics, optimizing industrial design cycles, creating adaptive virtual environments for VR/AR systems.