ZM-Net: Real-time Zero-shot Image Manipulation Network

TL;DR

ZM-Net introduces a fully-differentiable architecture capable of real-time, zero-shot image manipulation based on diverse guiding signals, including unseen ones, outperforming existing methods in efficiency and flexibility.

Contribution

The paper presents ZM-Net, the first model to enable real-time zero-shot image manipulation by jointly training a parameter generator and an image transformation network.

Findings

ZM-Net achieves high-quality manipulation in real-time.

It generalizes well to unseen guiding signals.

Constructed a large-scale style dataset with over 20,000 images.

Abstract

Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.