POBEVM: Real-time Video Matting via Progressively Optimize the Target Body and Edge
This work addresses a specific bottleneck in video matting for applications requiring real-time and accurate edge details, representing an incremental advancement with novel components for edge optimization.
The paper tackles the problem of fuzzy or incorrect target edges in video matting by proposing a method that separately and progressively optimizes the target body and edge, achieving significant improvements in edge quality and outperforming prior trimap-free methods on datasets like Distinctions-646 and VideoMatte240K.
Deep convolutional neural networks (CNNs) based approaches have achieved great performance in video matting. Many of these methods can produce accurate alpha estimation for the target body but typically yield fuzzy or incorrect target edges. This is usually caused by the following reasons: 1) The current methods always treat the target body and edge indiscriminately; 2) Target body dominates the whole target with only a tiny proportion target edge. For the first problem, we propose a CNN-based module that separately optimizes the matting target body and edge (SOBE). And on this basis, we introduce a real-time, trimap-free video matting method via progressively optimizing the matting target body and edge (POBEVM) that is much lighter than previous approaches and achieves significant improvements in the predicted target edge. For the second problem, we propose an Edge-L1-Loss (ELL) function that enforces our network on the matting target edge. Experiments demonstrate our method outperforms prior trimap-free matting methods on both Distinctions-646 (D646) and VideoMatte240K(VM) dataset, especially in edge optimization.