activity
20182020
most citedGetting Topology and Point Cloud Generation to Mesh

2 citations · 3 across the 4 of their papers we have counts for

collaborators

8 papers

cs.RO20201 cited

Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists

Ardavan Bidgoli, Manuel Ladron De Guevara, Cinnie Hsiung +2

Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniq…

cs.GR2020

Learned Interpolation for 3D Generation

Austin Dill, Songwei Ge, Eunsu Kang +2

In order to generate novel 3D shapes with machine learning, one must allow for interpolation. The typical approach for incorporating this creative process is to interpolate in a le…

cs.LG20192 cited

Getting Topology and Point Cloud Generation to Mesh

Austin Dill, Chun-Liang Li, Songwei Ge +1

In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface,…

cs.CV2019

LucidDream: Controlled Temporally-Consistent DeepDream on Videos

Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos

In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by…

cs.LG2019

Developing Creative AI to Generate Sculptural Objects

Songwei Ge, Austin Dill, Eunsu Kang +4

We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical detai…

cs.LG2019

The Myths of Our Time: Fake News

Vít Růžička, Eunsu Kang, David Gordon +3

While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities a…