12 citations · 12 across the 6 of their papers we have counts for
8 papers
Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization
Yuechen Zhang, Zexin He, Jinbo Xing +2
Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Ba…
Video Coding Using Learned Latent GAN Compression
Mustafa Shukor, Bharath Bhushan Damodaran, Xu Yao +1
We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intr…
Semantic Unfolding of StyleGAN Latent Space
Mustafa Shukor, Xu Yao, Bharath Bushan Damodaran +1
Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image…
Feature-Style Encoder for Style-Based GAN Inversion
Xu Yao, Alasdair Newson, Yann Gousseau +1
We propose a novel architecture for GAN inversion, which we call Feature-Style encoder. The style encoder is key for the manipulation of the obtained latent codes, while the featur…
Semantic and Geometric Unfolding of StyleGAN Latent Space
Mustafa Shukor, Xu Yao, Bharath Bhushan Damodaran +1
Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to a natural image. Th…
A Latent Transformer for Disentangled Face Editing in Images and Videos
Xu Yao, Alasdair Newson, Yann Gousseau +1
High quality facial image editing is a challenging problem in the movie post-production industry, requiring a high degree of control and identity preservation. Previous works that…
High Resolution Face Age Editing
Xu Yao, Gilles Puy, Alasdair Newson +2
Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some o…
Photo style transfer with consistency losses
Xu Yao, Gilles Puy, Patrick Pérez
We address the problem of style transfer between two photos and propose a new way to preserve photorealism. Using the single pair of photos available as input, we train a pair of d…