AI Real‐Time Compositing Algorithm for Film and Television Special Effects Based on Latent Space Diffusion Model

Artificial intelligence–driven visual effects have become increasingly important in film and television production; however, achieving high‐quality real‐time compositing with temporal stability and visual realism remains a significant challenge. This study proposes a novel real‐time compositing framework based on a latent space diffusion model to efficiently integrate foreground and background elements while preserving structural consistency across frames. The pipeline employs a Matting Objective Decomposition Network (MODNet) to extract the foreground and corresponding alpha matte, followed by latent space encoding and diffusion‐based compositing using conditional reverse denoising. To enhance temporal coherence, a temporal attention mechanism is incorporated to ensure frame‐to‐frame consistency. Additionally, a Deep Image Harmonization Network (DIHNet) is applied to align illumination and color between composited elements, improving perceptual realism. The proposed method is evaluated on Human‐Centric Video Matting and HYouTube datasets under diverse lighting and motion conditions. Experimental results demonstrate superior performance, achieving a Peak Signal‐to‐Noise Ratio (PSNR) of 41.2 dB, Structural Similarity Index Measure (SSIM) of 0.958, Learned Perceptual Image Patch Similarity (LPIPS) of 0.067, and Fréchet Inception Distance (FID) of 13.1. The framework operates in real time at 31.4–36 frames per second with low latency. These results confirm the effectiveness of the proposed approach for high‐quality, temporally stable, and efficient video compositing in practical visual effects applications.