Fast Soft Color Segmentation

TL;DR

This paper introduces a neural network-based method for fast soft color image segmentation into RGBA layers, enabling real-time applications like video editing with high quality and significantly improved speed.

Contribution

The authors propose a single-pass neural network approach for soft color segmentation that outperforms previous iterative methods in speed while maintaining comparable quality.

Findings

Achieves 300,000x faster inference than previous methods.

Produces qualitative and quantitative results comparable to state-of-the-art.

Demonstrates effective application in video editing scenarios.

Abstract

We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions. The resulting layers from decomposition pave the way for applications that benefit from layer-based editing, such as recoloring and compositing of images and videos. The current state-of-the-art approach for this problem is hindered by slow processing time due to its iterative nature, and consequently does not scale to certain real-world scenarios. To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass. Furthermore, our method separately decomposes the color layers and the alpha channel layers. By leveraging a novel training objective, our method achieves proper assignment of colors amongst layers. As a consequence, our method achieve promising quality without existing issue of inference speed for iterative approaches. Our thorough experimental analysis shows that our method produces qualitative and quantitative results comparable to previous methods while achieving a 300,000x speed improvement. Finally, we utilize our proposed method on several applications, and demonstrate its speed advantage, especially in video editing.