Adobe Researchers Publish 72 Papers at Top Computer Vision and Graphics Conferences in 2019

October 25, 2019

Image from Shape Unicode: A Unified Shape Representation

This year, Adobe Research once again excelled in developing and disseminating innovative new technologies, with Adobe authors publishing 72 research papers at the top computer vision and graphics conferences in 2019:

  • CVPR 2019 (Conference on Computer Vision and Pattern Recognition): Adobe authors contributed to 24 papers
  • SIGGRAPH 2019 (ACM Special Interest Group on Computer Graphics and Interactive Techniques): Adobe authors contributed to 13 papers, plus 2 Transactions on Graphics (TOG) papers
  • ICCV 2019 (International Conference on Computer Vision): Adobe authors contributed to 25 papers
  • SIGGRAPH Asia 2019: Adobe authors contributed to 8 papers

Nearly all of these papers emerged from university collaborations. Most were first-authored by university PhD students following successful summer internships working with Adobe researchers. (Find instructions on applying for a 2019 internship here.

Adobe Research authors participated in these conferences in other ways, including co-organizing workshops and tutorials, presenting at workshops, and presenting technical demos. 

This is in addition to publications in numerous related and overlapping areas, including human-computer interaction (UIST, CHI), machine learning (NeurIPS, ICLR), and others.

Here is a sampling of Adobe Research’s 2019 papers in computer vision and graphics:


Detecting Photoshopped Faces by Scripting Photoshop
Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
ICCV 2019
[ Paper ] [ Project ] [ Code ]

Shape Unicode: A Unified Shape Representation
Sanjeev Muralikrishnan, Vladimir Kim, Matthew Fisher, Siddhartha Chaudhuri
CVPR 2019
[ Paper ]

Stylizing Video by Example
Ondřej Jamriška, Šárka Sochorová, Ondřej Texler, Michal Lukáč, Jakub Fišer, Jingwan Lu, Eli Shechtman, Daniel Sýkora
SIGGRAPH 2019
[ Paper ] [ Webpage ] [ Video ]

Deep View Synthesis from Sparse Photometric Images
Zexiang Xu, Sai Bi, Kalyan Sunkavalli, Sunil Hadap, Hao Su, Ravi Ramamoorthi
SIGGRAPH 2019
[ Paper] [ Video ]

Video Object Segmentation Using Space-Time Memory Networks
Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
ICCV 2019 (oral)
[ Paper ] [ Video ] [ Max Sneak ]

Multi-view Relighting Using a Geometry-Aware Network
Julien Philip, Michaël Gharbi, Tinghui Zhou, Alexei A. Efros, George Drettakis
SIGGRAPH 2019
[ Paper] [ Video ]

Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
Senthil Purushwalkam, Abhinav Gupta, Danny Kaufman, Bryan Russell
ICLR 2019
[ Webpage ]

Automatically Translating Image Processing Libraries to Halide
Maaz Bin Safeer Ahmad, Jonathan Ragan-Kelley, Alvin Cheung and Shoaib Kamil
SIGGRAPH ASIA 2019
[ Paper ]

LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, Tingfa Xu
ICLR 2019
[ Paper ]

3D Ken Burns Effect from a Single Image
Simon Niklaus, Long Mai, Jimei Yang, Feng Li
SIGGRAPH Asia 2019
[ Paper ] [ Webpage ] [ Video ] [ MAX Sneak ]
Image from research demo based on Video Object Segmentation Using Space-Time Memory Networks

Related Posts