no code implementations • CVPR 2022 • Zhongzheng Ren, Aseem Agarwala, Bryan Russell, Alexander G. Schwing, Oliver Wang
We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF).
1 code implementation • CVPR 2020 • Hoang Le, Feng Liu, Shu Zhang, Aseem Agarwala
We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent.
2 code implementations • CVPR 2019 • Raviteja Vemulapalli, Aseem Agarwala
Most of the existing work on automatic facial expression analysis focuses on discrete emotion recognition, or facial action unit detection.
3 code implementations • ICCV 2017 • Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala
We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow.
Ranked #2 on Video Prediction on DAVIS 2017
no code implementations • 30 Nov 2016 • Raymond Yeh, Ziwei Liu, Dan B. Goldman, Aseem Agarwala
High-level manipulation of facial expressions in images --- such as changing a smile to a neutral expression --- is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face.
1 code implementation • 12 Jul 2015 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers.
Ranked #1 on Font Recognition on VFR-Wild
no code implementations • 31 Mar 2015 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
We address a challenging fine-grain classification problem: recognizing a font style from an image of text.
no code implementations • 18 Dec 2014 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
We present a domain adaption framework to address a domain mismatch between synthetic training and real-world testing data.
no code implementations • ACM Transactions on Graphics 2014 • Peter O'Donovan, Jānis Lībeks, Aseem Agarwala, Aaron Hertzmann
These tools are complementary; a user may search for "graceful" fonts, select a reasonable one, and then refine the results from a list of fonts similar to the selection.
no code implementations • CVPR 2014 • Guang Chen, Jianchao Yang, Hailin Jin, Jonathan Brandt, Eli Shechtman, Aseem Agarwala, Tony X. Han
This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content.
Ranked #1 on Font Recognition on VFR-447
1 code implementation • 15 Nov 2013 • Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, Holger Winnemoeller
The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research.