Novel view synthesis is attractive for social media, but it often contains unwanted details such as personal information that needs to be edited out for a better experience. Multiplane image (MPI) is desirable for social media because of its generality but it is complex and computationally expensive, making object removal challenging. To address these challenges, we propose CORE-MPI, which employs embedding images to improve the consistency and accessibility of MPI object removal. CORE-MPI allows for real-time transmission and interaction with embedding images on social media, facilitating object removal with a single mask. However, recovering the geometric information hidden in the embedding images is a significant challenge. Therefore, we propose a dual-network approach, where one network focuses on color restoration and the other on inpainting the embedding image including geometric information. For the training of CORE-MPI, we introduce a pseudo-reference loss aimed at proficient color recovery, even in complex scenes or with large masks. Furthermore, we present a disparity consistency loss to preserve the geometric consistency of the inpainted region. We demonstrate the effectiveness of CORE-MPI on RealEstate10K and UCSD datasets.

Recently, alpha matting has witnessed remarkable growth by wide and deep convolutional neural networks. However, previous deep learning-based alpha matting methods require a high computational cost to be used in real environments including mobile devices. In this letter, a lightweight natural image matting network with a similarity-preserving knowledge distillation is developed. The similarity-preserving knowledge distillation makes pairwise similarities from a compact student network similar to those from a teacher network. The pairwise similarity measured on spatial, channel, and batch units enables to transfer knowledge of the teacher to the student. Based on the similarity-preserving knowledge distillation, we not only design a lighter and smaller student network than the teacher one but also achieve superior performance compared to that of the student network without the knowledge distillation. In addition, the proposed algorithm can be seamlessly applied to various deep image matting algorithms. Therefore, our algorithm is effective for mobile applications (e.g., human portrait matting), which are in growing demand. The effectiveness of the proposed algorithm is verified on two public benchmark datasets.