Nonlinear color blending, which is advanced blending indicated by blend modes such as "overlay" and "multiply," is extensively employed by digital creators to produce attractive visual effects. To enjoy such flexible editing modalities on existing bitmap images like photographs, however, creators need a fast nonlinear blending algorithm that decomposes an image into a set of semi-transparent layers. To address this issue, we propose a neural-network-based method for nonlinear decomposition of an input image into linear and nonlinear alpha layers that can be separately modified for editing purposes, based on the specified color palettes and blend modes. Experiments show that our proposed method achieves an inference speed 370 times faster than the state-of-the-art method of nonlinear image unblending, which uses computationally intensive iterative optimization. Furthermore, our reconstruction quality is higher or comparable than other methods, including linear blending models. In addition, we provide examples that apply our method to image editing with nonlinear blend modes.

Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. Recent works attempt to edit an image by manipulating its under-lying latent code, but rarely go beyond the basic task of at-tribute adjustment. We propose the first method that enables manipulation with multidimensional condition such as key-points and captions. Specifically, we design an algorithm that searches for a new latent code that satisfies the target condition based on the Surrogate Gradient Field (SGF) induced by an auxiliary mapping network. For quantitative comparison, we propose a metric to evaluate the disentanglement of manipulation methods. Thorough experimental analysis on the facial attribute adjustment task shows that our method outperforms state-of-the-art methods in disentanglement. We further apply our method to tasks of various condition modalities to demonstrate that our method can alter complex image properties such as keypoints and captions.

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.