Intelligent rendering optimization for animated scenes using reinforcement learning and computer vision

Real-time rendering of complex animated scenes is computationally intensive and often struggles to balance visual fidelity and performance in dynamic or densely populated environments, as conventional pipelines lack adaptive resource optimization, leading to reduced frame rates and degraded visual quality. This research proposes an intelligent rendering framework integrating Computer Vision (CV) and Reinforcement Learning (RL) through the Adaptive Seeker-Twin Delayed Deep Deterministic Policy Gradient (AdapS-Twin D3PG) algorithm. The ATD-12 dataset consists of 10,000 collected animation image triplets designed for video frame interpolation and motion analysis tasks, and is utilized for training and evaluation. To enhance data quality, Savitzky-Golay filtering is applied to smooth motion trajectories while preserving essential temporal patterns, and Min-Max normalization standardizes frame color and intensity values for consistent input representation. Feature extraction using Histogram of Oriented Gradients (HOG) captures structural and shape-based information, enabling the RL agent to identify high-priority regions for selective rendering refinement. The proposed AdapS-Twin D3PG dynamically adjusts rendering parameters, refining critical regions while coarsely processing less significant areas to optimize computational efficiency. Experimental results from the Python-implemented framework demonstrate that the proposed AdapS-Twin D3PG model achieves superior quantitative performance with Accuracy (95.48), Recall (93.72), F1 Score (93.94), and AUC (96.82). The system also delivers enhanced visual reconstruction quality, evidenced by a PSNR of 39.4 dB and a low FID of 13.8, while maintaining an efficient rendering time of 4.2 seconds, thereby confirming its effectiveness in optimizing both visual fidelity and computational efficiency for real-time animated scene rendering.