28 citations · 49 across the 5 of their papers we have counts for
5 papers · 1 filter
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
Zekun Hao, Arun Mallya, Serge Belongie +1
We present GANcraft, an unsupervised neural rendering framework for generating photorealistic images of large 3D block worlds such as those created in Minecraft. Our method takes a…
Learning Gradient Fields for Shape Generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor +4
In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concen…
DualSDF: Semantic Shape Manipulation using a Two-Level Representation
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely +1
We are seeing a Cambrian explosion of 3D shape representations for use in machine learning. Some representations seek high expressive power in capturing high-resolution detail. Oth…
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Guandao Yang, Xun Huang, Zekun Hao +3
As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point…
Scale-Aware Face Detection
Zekun Hao, Yu Liu, Hongwei Qin +3
Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a larg…