704 citations · 4.1k across the 112 of their papers we have counts for
12 papers · 1 filter
GrabAR: Occlusion-aware Grabbing Virtual Objects in AR
Xiao Tang, Xiaowei Hu, Chi-Wing Fu +1
Existing augmented reality (AR) applications often ignore occlusion between real hands and virtual objects when incorporating virtual objects in our views. The challenges come from…
PU-GAN: a Point Cloud Upsampling Adversarial Network
Ruihui Li, Xianzhi Li, Chi-Wing Fu +2
Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based o…
Cross-Domain Cascaded Deep Feature Translation
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
In recent years we have witnessed tremendous progress in unpaired image-to-image translation methods, propelled by the emergence of DNNs and adversarial training strategies. Howeve…
Learning Character-Agnostic Motion for Motion Retargeting in 2D
Kfir Aberman, Rundi Wu, Dani Lischinski +2
Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer anim…
Unsupervised Detection of Distinctive Regions on 3D Shapes
Xianzhi Li, Lequan Yu, Chi-Wing Fu +2
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct…
Blind Visual Motif Removal from a Single Image
Amir Hertz, Sharon Fogel, Rana Hanocka +2
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image. For example, deco…
Image Resizing by Reconstruction from Deep Features
Moab Arar, Dov Danon, Daniel Cohen-Or +1
Traditional image resizing methods usually work in pixel space and use various saliency measures. The challenge is to adjust the image shape while trying to preserve important cont…
Implicit Pairs for Boosting Unpaired Image-to-Image Translation
Yiftach Ginger, Dov Danon, Hadar Averbuch-Elor +1
In image-to-image translation the goal is to learn a mapping from one image domain to another. In the case of supervised approaches the mapping is learned from paired samples. Howe…
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
Felix Petersen, Amit H. Bermano, Oliver Deussen +1
The long-coveted task of reconstructing 3D geometry from images is still a standing problem. In this paper, we build on the power of neural networks and introduce Pix2Vex, a networ…
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Kangxue Yin, Zhiqin Chen, Hui Huang +2
We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and c…
PointWise: An Unsupervised Point-wise Feature Learning Network
Matan Shoef, Sharon Fogel, Daniel Cohen-Or
We present a novel approach to learning a point-wise, meaningful embedding for point-clouds in an unsupervised manner, through the use of neural-networks. The domain of point-cloud…
CrossNet: Latent Cross-Consistency for Unpaired Image Translation
Omry Sendik, Dani Lischinski, Daniel Cohen-Or
Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation. In particular, it was shown that a wide variety o…