Reconstructing 3D meshes of objects from 2D images is an important but challenging task. Previous 3D reconstruction methods either only focus on generating the mesh from a single image, or multi-view images. Instead of investigating these problems separately, we present a novel view attention guided network called VANet which addresses both single and multi-view 3D reconstruction under a unified framework. To explore non-visible parts of an object during the reconstruction, a channel-wise view attention mechanism and a dual pathway network architecture are introduced. The proposed network highlights the informative object parts and compensates those non-informative ones with auxiliary views of input.

In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance. Given the inefficiency of standard CNNs in handling large spatial transformation, we propose a structure-aware flow based method for high-quality person image generation. Specifically, instead of learning the complex overall pose changes of human body, we decompose the human body into different semantic parts (e.g., head, torso, and legs) and apply different networks to predict the flow fields for these parts separately. Moreover, we carefully design the network modules to effectively capture the local and global semantic correlations of features within and among the human parts respectively. Extensive experimental results show that our method can generate high-quality results under large pose discrepancy and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.

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Word embedding has been widely used in word sense disambiguation (WSD) and many other tasks in recent years for it can well represent the semantics of words. However, the existing word embedding methods mostly represent each word as a single vector, without considering the homonymy and polysemy of the word; thus, their performances are limited. In order to address this problem, an effective topical word embedding (TWE)-based WSD method, named TWE-WSD, is proposed, which integrates Latent Dirichlet Allocation (LDA) and word embedding. Instead of generating a single word vector (WV) for each word, TWE-WSD generates a topical WV for each word under each topic. Effective integrating strategies are designed to obtain high quality contextual vectors. Extensive experiments on SemEval-2013 and SemEval-2015 for English all-words tasks showed that TWE-WSD outperforms other state-of-the-art WSD methods, especially on nouns. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.