94 citations · 431 across the 15 of their papers we have counts for
4 papers · 1 filter
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
Manyi Li, Akshay Gadi Patil, Kai Xu +7
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently. Our key observation…
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
Fenggen Yu, Yan Zhang, Kai Xu +2
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision. Given a co…
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
Kangxue Yin, Hui Huang, Daniel Cohen-Or +1
We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons…
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
Zili Yi, Zhiqin Chen, Hao Cai +3
We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations…