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Found 9 papers, 2 papers with code
Texturize a GAN Using a Single Image
Can we customize a deep generative model which can generate images that can match the texture of some given image?
DisUnknown: Distilling Unknown Factors for Disentanglement Learning
In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor.
Revisiting the Continuity of Rotation Representations in Neural Networks
In this paper, we provide some careful analysis of certain pathological behavior of Euler angles and unit quaternions encountered in previous works related to rotation representation in neural networks.
Generative Tweening: Long-term Inbetweening of 3D Human Motions
We introduce a biomechanically constrained generative adversarial network that performs long-term inbetweening of human motions, conditioned on keyframe constraints.
One-Shot Identity-Preserving Portrait Reenactment
This is achieved by a novel landmark disentanglement network (LD-Net), which predicts personalized facial landmarks that combine the identity of the target with expressions and poses from a different subject.
Disentangling Style and Content in Anime Illustrations
We propose a novel Generative Adversarial Disentanglement Network which can disentangle two complementary factors of variations when only one of them is labelled in general, and fully decompose complex anime illustrations into style and content in particular.
Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks
Deep learning-based style transfer between images has recently become a popular area of research.
Realistic Dynamic Facial Textures From a Single Image Using GANs
By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.
On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
We further demonstrate the stability of WN on a 21-layer ResNet trained with the CelebA data set.
