The goal of digital dress body animation is to produce the most realistic dress body animation possible. Although a method based on the same topology as the body can produce realistic results, it can only be applied to garments with the same topology as the body. Although the generalization-based approach can be extended to different types of garment templates, it still produces effects far from reality. We propose GSNet, a learning-based model that generates realistic garment animations and applies to garment types that do not match the body topology. We encode garment templates and body motions into latent space and use graph convolution to transfer body motion information to garment templates to drive garment motions. Our model considers temporal dependency and provides reliable physical constraints to make the generated animations more realistic. Qualitative and quantitative experiments show that our approach achieves state-of-the-art 3D garment animation performance.

Abstract Fashion image retrieval is an important branch of image retrieval technology. With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text‐based to content‐based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi‐scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high‐dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high‐dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state‐of‐the‐art methods.

Context: Current image inpainting methods show great effects in different applications such as image editing, object removal, art creation and soon, but lack of editability of the inpainting results and convincing unsupervised features.Objective: To improve the existing methods, an optimized framework for image inpainting purpose is proposed based on hierarchical variational auto-encoder (VAE) as well as some optimization strategies.Method: Firstly, the VAE is used to extract the distribution of the features of the masked image in different scales, however, it will cause the distribution offset of extracted features which is unfavorable for image inpainting.Therefore, an optimal strategy that sampling the effective feature and invalid feature separately to avoid the offset of feature distribution of the masked image is integrated into the framework.To further improve the formulation of the proposed framework, the same encoder is used to realize the conversion from two domains to the same domain, which is a benefit to enhance the extraction of effective feature regions.In addition, we also introduce the cycle consistency constraints and GAN constraints into the framework to supervise the inpainting process.Result: Experimental results on the available image dataset demonstrate the effectiveness and superiority of the proposed framework.

The approaches based on laboratory experiments to explore the interactions between viruses and their hosts are costly and time-consuming. Due to advances in high-throughput technologies, recent computational methods to predict virus-host interaction have attracted increasing attention. But these methods couldn’t effectively utilize the heterogeneous information of the virus-host interaction network. In this paper, we propose a computational method to predict potential virus-host interaction via network fusion and graph mining, named VHINFGM. Different from existing methods, VHINFGM constructs two different heterogeneous networks from the existing interaction network through similarity network fusion and graph embedding technique. Then, VHINFGM introduces two kinds of meta-path scores to extract features from each heterogeneous graph. Based on this graph mining approach, a mixed feature vector for two heterogeneous networks can be obtained, which can be used as the input of a classifier to predict potential interactions. VHINFGM is verified on four datasets, it can be found that VHINFGM outperforms the state-of-the-art methods. 5 out of the top 10 virus-host interaction reported by VHINFGM has been validated in the biological experiments. Besides, VHINFGM predicts 5 new virus-host relationships, which could guide further research.