MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
Cheng-Han Lee,Ziwei Liu,Lingyun Wu,Ping Luo +3 more
- pp 5549-5558
Reads0
Chats0
TLDR
MaskGAN as mentioned in this paper proposes MaskGAN to enable diverse and interactive face manipulation by learning style mapping between a free-form user modified mask and a target image, enabling diverse generation results.Abstract:
Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBST). Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results. EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs. Specifically, it introduces dual-editing consistency as the auxiliary supervision signal. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ. MaskGAN is comprehensively evaluated on two challenging tasks: attribute transfer and style copy, demonstrating superior performance over other state-of-the-art methods. The code, models, and dataset are available at https://github.com/switchablenorms/CelebAMask-HQ.read more
Citations
More filters
Posted Content
Self-Supervised 3D Face Reconstruction via Conditional Estimation.
TL;DR: In this paper, a conditional estimation framework is proposed to learn semantically meaningful 3D facial parameters from 2D single-view images by self-supervised training from videos, where the estimation of any facial parameter is not only conditioned on the given image, but also on the facial parameters that have already been derived, and reflectance symmetry and consistency among the video frames are adopted to improve the disentanglement of facial parameters.
SIRA: Relightable Avatars from a Single Image Supplemental Document
TL;DR: In this article , the spherical gaussians are used to express all the terms in the rendering equation and the integral is computed over the hemisphere Ω = {ωi : ωi · n > 0}.
Journal ArticleDOI
Image Classifiers Leak Sensitive Attributes About Their Classes
Lukas Struppek,Dominik Hintersdorf,Felix Friedrich,Manuel Brack,Patrick Schramowski,Kristian Kersting +5 more
TL;DR: The authors leverages text-to-image synthesis to infer sensitive attributes of individual classes in a black-box setting, while remaining competitive with related white-box attacks, and demonstrate that adversarial robust models are even more vulnerable to such privacy leakage than standard models.
Book ChapterDOI
Generator from Edges: Reconstruction of Facial Images
Nao Takano,Gita Alaghband +1 more
TL;DR: In this paper, a generator from edges (GFE) was proposed to generate paired images with the canny edge, which can be used to reconstructing a facial image from a pencil sketch.
Proceedings ArticleDOI
Controllable Facial Micro-element Synthesis using Segmentation Maps
TL;DR: In this article , a deep learning-based high-resolution image synthesis model is employed after generating a label image from the face RGB image through skin micro-element segmentation and face attribute segmentation.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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.