Original image Instance segmentation Instance 1 trimap Instance 2 trimap Alpha matte 1 Alpha matte 2 Instance 1 Instance 2 Composite 1 Composite 2 Composite all Figure 1 : Our approach first generates coarse instance masks that are used to create trimaps. Then, the trimaps and the original image are used by the image matting network to produce an alpha matte for each instance. Finally, the alpha mattes are used for image compositing. Abstract-Image compositing is a key step in film making and image editing that aims to segment a foreground object and combine it with a new background. Automatic image compositing can be done easily in a studio using chromakeying when the background is pure blue or green. However, image compositing in natural scenes with complex backgrounds remains a tedious task, requiring experienced artists to handsegment. In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task. Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method. It extends automatic image compositing techniques such as chroma-keying to scenes with complex natural backgrounds without the need for any kind of user interaction. The output of our approach can be considered as both refined instance segmentations and alpha mattes with semantic meanings. We provide experimental results which show improved performance results as compared to existing approaches.
Hu et el. REF proposed an integration of instance segmentation and image matting processes to generate alpha mattes.
Instance Segmentation Based Semantic Matting for Compositing Applications
2019 16th Conference on Computer and Robot Vision (CRV)
2019 16th Conference on Computer and Robot Vision (CRV)
Computer Science