Open AccessProceedings Article
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.
Xun Huang,Serge Belongie +1 more
Reads0
Chats0
TLDR
In this article, adaptive instance normalization (AdaIN) is proposed to align the mean and variance of the content features with those of the style features, which enables arbitrary style transfer in real-time.Abstract:
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.read more
Citations
More filters
Journal ArticleDOI
Gated SwitchGAN for multi-domain facial image translation
TL;DR: A switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains and introduces a new capability of the generator that represents attribute intensity control and extracts content information without tailored training.
Journal ArticleDOI
PortraitNET: Photo-realistic portrait cartoon style transfer with self-supervised semantic supervision
TL;DR: This work re-define the semantics as the pixel motion field according to the color displacement between adjacent animation frames along the optical direction and initiatively propose the self-supervised semantic network (SSNet) to learn semantic maps without human inference or any priories.
Posted Content
A Unified Framework for Generalizable Style Transfer: Style and Content Separation
Yexun Zhang,Ya Zhang,Wenbin Cai +2 more
TL;DR: A unified style transfer framework that consists of style encoder, contentEncoder, mixer and decoder is proposed that enables building one single transfer network for multiple styles and further leads to a key merit of the framework, i.e. its generalizability to new styles and contents.
Posted Content
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis
TL;DR: This work proposes an approach to learn neural representation of camera poses and 3D scenes, coupled with neural representations of local camera movements, represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose.
Posted Content
Task-agnostic Temporally Consistent Facial Video Editing.
Meng Cao,Haozhi Huang,Hao Wang,Xuan Wang,Li Shen,Wang Sheng,Linchao Bao,Zhifeng Li,Jiebo Luo +8 more
TL;DR: This paper proposes a task-agnostic temporally consistent facial video editing framework based on a 3D reconstruction model that fully exploits both image and video datasets and enforces temporal consistency.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.