activity
20182020
most citedIntuitive, Interactive Beard and Hair Synthesis with Generative Models

2 citations · 2 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV2020

HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures

Yichao Zhou, Jingwei Huang, Xili Dai +4

We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution that are acc…

cs.CV20202 cited

Intuitive, Interactive Beard and Hair Synthesis with Generative Models

Kyle Olszewski, Duygu Ceylan, Jun Xing +4

We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challeng…

cs.CV2020

Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition

Tianlang Chen, Chen Fang, Xiaohui Shen +3

In this work, we propose a new solution to 3D human pose estimation in videos. Instead of directly regressing the 3D joint locations, we draw inspiration from the human skeleton an…

cs.CV2019

Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Yichao Zhou, Haozhi Qi, Yuexiang Zhai +4

In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities. Our me…

cs.GR2018

NeuralDrop: DNN-based Simulation of Small-Scale Liquid Flows on Solids

Rajaditya Mukherjee, Qingyang Li, Zhili Chen +2

Small-scale liquid flows on solid surfaces provide convincing details in liquid animation, but they are difficult to be simulated with efficiency and fidelity, mostly due to the co…

cs.CV2018

Learning to Sketch with Deep Q Networks and Demonstrated Strokes

Tao Zhou, Chen Fang, Zhaowen Wang +5

Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simula…