High-resolution portrait video matting algorithm based on Siamese network

In recent years, video matting has become a hot topic across various industries, especially with the extensive use of portrait video matting. Therefore, achieving precise and rapid matting results has become a focal point of recent research. While conventional image matting can relatively accurately obtain precise foreground portraits, the results are still suboptimal for video matting. This paper uses video as the sole input, and by incorporating a triplet generation module and a spatiotemporal feature aggregation module, we embed a twin network into our architecture to achieve faster and more accurate portrait video matting under temporal consistency conditions. Comparative results demonstrate that, compared to the Deep Image Matting (DIM) method, our approach reduces MSE by 73.3%, SAD by 51.3%, Grad by 6.9%, Diff by 73.6%, and dtSSD by 87.6%.