153 citations · 517 across the 27 of their papers we have counts for
5 papers · 1 filter
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Zongze Wu, Dani Lischinski, Eli Shechtman
We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first sh…
Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects
Jiahui Lyu, Bojian Wu, Dani Lischinski +2
Capturing the 3D geometry of transparent objects is a challenging task, ill-suited for general-purpose scanning and reconstruction techniques, since these cannot handle specular li…
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
Mingyi Shi, Kfir Aberman, Andreas Aristidou +4
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video.While previous methods rely on either rigging or inve…
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Jinming Cao, Yangyan Li, Mingchao Sun +5
Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise c…
Unsupervised multi-modal Styled Content Generation
Omry Sendik, Dani Lischinski, Daniel Cohen-Or
The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content, both in 2D and in 3D. Generative Adversarial Networks…