Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read

High-Resolution Deep Image Matting

Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such approaches set state-of-the-arts in image matting; however, they may fail in real-world matting applications due to hardware limitations, since real-world input images for matting are mostly of very high resolution. In this paper, we propose HDMatt, a first deep learning based image matting approach for high-resolution inputs. More concretely, HDMatt runs matting in a patch-based crop-and-stitch manner for high-resolution inputs with a novel module design to address the contextual dependency and consistency issues between different patches. Compared with vanilla patch-based inference which computes each patch independently, we explicitly model the cross-patch contextual dependency with a newly-proposed Cross-Patch Contextual module (CPC) guided by the given trimap. Extensive experiments demonstrate the effectiveness of the proposed method and its necessity for high-resolution inputs. Our HDMatt approach also sets new state-of-the-art performance on Adobe Image Matting and AlphaMatting benchmarks and produce impressive visual results on more real-world high-resolution images.Comment: AAAI 202

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Natural image matting is an important problem in computer vision and graphics. It is an ill-posed problem when only an input image is available without any external information. While the recent deep learning approaches have shown promising results, they only estimate the alpha matte. This paper presents a context-aware natural image matting method for simultaneous foreground and alpha matte estimation. Our method employs two encoder networks to extract essential information for matting. Particularly, we use a matting encoder to learn local features and a context encoder to obtain more global context information. We concatenate the outputs from these two encoders and feed them into decoder networks to simultaneously estimate the foreground and alpha matte. To train this whole deep neural network, we employ both the standard Laplacian loss and the feature loss: the former helps to achieve high numerical performance while the latter leads to more perceptually plausible results. We also report several data augmentation strategies that greatly improve the network's generalization performance. Our qualitative and quantitative experiments show that our method enables high-quality matting for a single natural image. Our inference codes and models have been made publicly available at https://github.com/hqqxyy/Context-Aware-Matting.Comment: This is the camera ready version of ICCV2019 pape

Rethinking Context Aggregation in Natural Image Matting

For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background. Exiting deep learning-based methods exploit specifically designed context aggregation modules to refine encoder features. However, the effectiveness of these modules has not been thoroughly explored. In this paper, we conduct extensive experiments to reveal that the context aggregation modules are actually not as effective as expected. We also demonstrate that when learned on large image patches, basic encoder-decoder networks with a larger receptive field can effectively aggregate context to achieve better performance.Upon the above findings, we propose a simple yet effective matting network, named AEMatter, which enlarges the receptive field by incorporating an appearance-enhanced axis-wise learning block into the encoder and adopting a hybrid-transformer decoder. Experimental results on four datasets demonstrate that our AEMatter significantly outperforms state-of-the-art matting methods (e.g., on the Adobe Composition-1K dataset, \textbf{25\%} and \textbf{40\%} reduction in terms of SAD and MSE, respectively, compared against MatteFormer). The code and model are available at \url{https://github.com/QLYoo/AEMatter}