Zeyu Huang 黄泽宇

I am currently a Ph.D. student in Computer Science supervised by Prof. Ruizhen Hu, working in Visual Computing Research Center, Shenzhen University.
Before that I got my B.Eng. in Software Engineering from Shenzhen University.

I am interested in Computer Graphics, Computer Vision and Robotics, especially on applying deep learning to synthesize graphics contents.

Email  /  CV  /  Google Scholar  /  Github

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Research

I have strong interests in graphics content synthesis. Specifically, my researching projects cover the following topics: 3D Reconstruction, Interaction Generation, Object Manipulation, Floorplan Generation, etc.

Spatial and Surface Correspondence Field for Interaction Transfer
Zeyu Huang, Honghao Xu Haibin Huang, Chongyang Ma, Hui Huang, Ruizhen Hu
SIGGRAPH, 2024

In this paper, we introduce a new method for the task of interaction transfer.

DINA: Deformable INteraction Analogy

Zeyu Huang, Sisi Dai Kai Xu, Hao Zhang, Hui Huang, Ruizhen Hu
GMOD, 2024
paper /

A means to generate interactions between two 3D objects with a descriptive and robust interaction representation.

ARO-Net: Learning Implicit Fields from Anchored Radial Observations
Yizhi Wang*, Zeyu Huang, Ariel Shamir, Hui Huang, Hao Zhang, Ruizhen Hu
CVPR, 2023
(*equal contribution)
project page / arXiv / code / video

A novel shape encoding for learning neural field representation of shapes that is category-agnostic and generalizable amid significant shape variations.

NIFT: Neural Interaction Field and Template for Object Manipulation
Zeyu Huang, Juzhan Xu, Sisi Dai, Kai Xu, Hao Zhang, Hui Huang, Ruizhen Hu
ICRA, 2023
project page / arXiv / code / video

A descriptive and robust interaction representation of object manipulations to facilitate imitation learning.

Graph2Plan: Learning Floorplan Generation from Layout Graphs
Ruizhen Hu, Zeyu Huang, Yuhan Tang, Oliver van Kaick, Hao Zhang, Hui Huang
SIGGRAPH, 2020
project page / arXiv / code / video

A learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human users to provide sparse design constraints.



Template adapted from Yizhi and Jon.