Multi-View Image Generation from a Single-View

Abstract

This paper addresses a challenging problem -- how to generate multi-view cloth images from only a single view input. To generate realistic-looking images with different views from the input, we propose a new image generation model termed VariGANs that combines the strengths of the variational inference and the Generative Adversarial Networks (GANs). Our proposed VariGANs model generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produce a coarse image with a different view. Conditioned on the generated low resolution images, it then proceeds to perform adversarial learning to fill details and generate images of consistent details with the input. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that images of a novel view generated by our model are more plausible than those generated by existing approaches, in terms of more consistent global appearance as well as richer and sharper details.

References in corpus (4)

• Conditional Generative Adversarial Nets
• DRAW: A Recurrent Neural Network For Image Generation
• Deep Convolutional Inverse Graphics Network
• Transformation-Grounded Image Generation Network for Novel 3D View Synthesis

Cited by in corpus (13)

• Progressive Pose Attention Transfer for Person Image Generation
• CR-GAN: Learning Complete Representations for Multi-view Generation
• Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
• Unsupervised Person Image Synthesis in Arbitrary Poses
• Generative Partial Multi-View Clustering
• Toward Characteristic-Preserving Image-based Virtual Try-On Network
• Recent Progress of Face Image Synthesis
• Multi-View Data Generation Without View Supervision
• Face Translation between Images and Videos using Identity-aware CycleGAN
• Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks
• View Extrapolation of Human Body from a Single Image
• Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
• Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning