Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
This addresses the problem of generating realistic images from different viewpoints for applications like robotics and AR/VR, though it appears incremental as it builds on existing GAN-based approaches.
The paper tackles cross-view image translation by proposing SelectionGAN, which generates natural scene images from arbitrary viewpoints using an input image and semantic map, achieving significantly better results than state-of-the-art methods on datasets like Dayton, CVUSA, and Ego2Top.
Cross-view image translation is challenging because it involves images with drastically different views and severe deformation. In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map. The proposed SelectionGAN explicitly utilizes the semantic information and consists of two stages. In the first stage, the condition image and the target semantic map are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using a multi-channel attention selection mechanism. Moreover, uncertainty maps automatically learned from attentions are used to guide the pixel loss for better network optimization. Extensive experiments on Dayton, CVUSA and Ego2Top datasets show that our model is able to generate significantly better results than the state-of-the-art methods. The source code, data and trained models are available at https://github.com/Ha0Tang/SelectionGAN.