1 code implementation • 12 Mar 2024 • Yuelong Li, Yafei Mao, Raja Bala, Sunil Hadap
Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage.
no code implementations • CVPR 2019 • Jinsong Zhang, Kalyan Sunkavalli, Yannick Hold-Geoffroy, Sunil Hadap, Jonathan Eisenmann, Jean-François Lalonde
We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network.
no code implementations • CVPR 2019 • Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-François Lalonde
We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image.
1 code implementation • ECCV 2018 • Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, Honglak Lee
Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode.
Ranked #7 on Human Pose Forecasting on Human3.6M (ADE metric)
no code implementations • 22 May 2018 • Minh Vo, Ersin Yumer, Kalyan Sunkavalli, Sunil Hadap, Yaser Sheikh, Srinivasa Narasimhan
Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams.
no code implementations • CVPR 2018 • Yannick Hold-Geoffroy, Kalyan Sunkavalli, Jonathan Eisenmann, Matt Fisher, Emiliano Gambaretto, Sunil Hadap, Jean-François Lalonde
This network is trained using automatically generated samples from a large-scale panorama dataset, and considerably outperforms other methods, including recent deep learning-based approaches, in terms of standard L2 error.
no code implementations • ICCV 2017 • Zhuo Hui, Kalyan Sunkavalli, Joon-Young Lee, Sunil Hadap, Jian Wang, Aswin C. Sankaranarayanan
A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF.
no code implementations • CVPR 2018 • Zhuo Hui, Kalyan Sunkavalli, Sunil Hadap, Aswin C. Sankaranarayanan
Real-world lighting often consists of multiple illuminants with different spectra.
2 code implementations • CVPR 2017 • Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.
no code implementations • 2 Dec 2016 • Jiajun Lu, Kalyan Sunkavalli, Nathan Carr, Sunil Hadap, David Forsyth
First, it allows a user to directly manipulate various illumination.
1 code implementation • CVPR 2017 • Yannick Hold-Geoffroy, Kalyan Sunkavalli, Sunil Hadap, Emiliano Gambaretto, Jean-François Lalonde
We present a CNN-based technique to estimate high-dynamic range outdoor illumination from a single low dynamic range image.
Ranked #1 on Outdoor Light Source Estimation on SUN360
no code implementations • CVPR 2013 • Hyeongwoo Kim, Hailin Jin, Sunil Hadap, In-So Kweon
Our method is based on a novel observation that for most natural images the dark channel can provide an approximate specular-free image.