Prashanth Chandran

Prashanth Chandran

Associate Research Scientist

Publications

ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting

Published in International Conference on Computer Vision (ICCV), 2023

In this paper, we target the application scenario of capturing high-fidelity assets for neural relighting in controlled studio conditions, but without requiring a dense light stage. Instead, we leverage a small number of area lights commonly used in photogrammetry. [Project Page]

Cite

 @InProceedings{Xu_2023_ICCV,
 author    = {Xu, Yingyan and Zoss, Gaspard and Chandran, Prashanth and Gross, Markus and Bradley, Derek and Gotardo, Paulo},
 title     = {ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting},
 booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
 month     = {October},
 year      = {2023},
 pages     = {22581-22591}
 }

Continuous Landmark Detection With 3D Queries

Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023

We propose the first facial landmark detection network that can predict continuous, unlimited landmarks, allowing to specify the number and location of the desired landmarks at inference time. Our method combines a simple image feature extractor with a queried landmark predictor, and the user can specify any continuous query points relative to a 3D template face mesh as input. [Project Page]

Cite

 @InProceedings{Chandran_2023_CVPR,
 author    = {Chandran, Prashanth and Zoss, Gaspard and Gotardo, Paulo and Bradley, Derek},
 title     = {Continuous Landmark Detection With 3D Queries},
 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 month     = {June},
 year      = {2023},
 pages     = {16858-16867}
 }

Production-Ready Face Re-Aging for Visual Effects

Published in Siggraph Asia, 2022

We demonstrate how the simple U-Net, surprisingly, allows us to advance the state of the art for re-aging real faces on video, with unprecedented temporal stability and preservation of facial identity across variable expressions, viewpoints, and lighting conditions. [Project Page]

Cite

 @article{Zoss_2022, 
 author = {Zoss, Gaspard and Chandran, Prashanth and Sifakis, Eftychios 
 and Gross, Markus and Gotardo, Paulo and Bradley, Derek}, 
 title = {Production-Ready Face Re-Aging for Visual Effects}, 
 year = {2022}, 
 issue_date = {December 2022}, 
 publisher = {Association for Computing Machinery}, 
 address = {New York, NY, USA}, 
 volume = {41}, 
 number = {6}, 
 issn = {0730-0301}, 
 doi = {10.1145/3550454.3555520}, 
 journal = {ACM Trans. Graph.}, 
 month = {nov}, 
 articleno = {237}, 
 numpages = {12}, 
 keywords = {facial re-aging, image and video editing} 
 }

Learning Dynamic 3D Geometry and Texture for Video Face Swapping

Published in Pacific Graphics, 2022

We approach the problem of face swapping from the perspective of learning simultaneous convolutional facial autoencoders for the source and target identities, using a shared encoder network with identity-specific decoders. [Project Page]

Cite

 @article {Ott22a, 
 journal = {Computer Graphics Forum}, 
 title = {Learning Dynamic 3D Geometry and Texture for Video Face Swapping}, 
 author = {Otto, Christopher and Naruniec, Jacek and Helminger, 
 Leonhard and Etterlin, Thomas and Mignone, Graziana and 
 Chandran, Prashanth and Zoss, Gaspard and Schroers, Christopher 
 and Gross, Markus and Gotardo, Paulo and Bradley, Derek and Weber, Romann},
 year = {2022},
 pages={611-622},
 month={Oct},
 number={7},
 volume={41},
 publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
 ISSN = {1467-8659},
 DOI = {10.1111/cgf.14705}
 }

Facial Animation with Disentangled Identity and Motion using Transformers

Published in ACM/Eurographics Symposium on Computer Animation, 2022

We propose a 3D+time framework for modeling dynamic sequences of 3D facial shapes, representing realistic non-rigid motion during a performance. [Project Page]

Cite

 @article{https://doi.org/10.1111/cgf.14641, 
 author = {Chandran, Prashanth and Zoss, Gaspard 
 and Gross, Markus and Gotardo, Paulo and Bradley, Derek},
 title = {Facial Animation with Disentangled Identity 
 and Motion using Transformers}, journal = {Computer Graphics Forum}, 
 volume = {41},
 number = {8},
 pages = {267-277},
 doi = {https://doi.org/10.1111/cgf.14641},
 year = {2022}
 }

MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling

Published in Siggraph, 2022

We demonstrate how MoRF is a strong new step towards 3D morphable neural head modeling. [Project Page]

Cite

 @article{Morf_2022,
 author = {Wang, Daoye and Chandran, Prashanth and Zoss, Gaspard and Bradely, Derek and Gotardo, Paulo},
 title = {MoRF: Morphable Radiance Fields for Multiview Neural Head Modeling},
 year = {2022},
 issue_date = {July 2022},
 publisher = {Association for Computing Machinery},
 address = {New York, NY, USA},
 journal = {ACM Trans. Graph.},
 month = {jul},
 numpages = {9},
 }

Facial Hair Tracking for High Fidelity Performance Capture

Published in Siggraph, 2022

We demonstrate the proposed capture pipeline on a variety of different facial hair styles and lengths, ranging from sparse and short to dense full-beards. [Project Page]

Cite

 @article{10.1145/3528223.3530116,
 author = {Winberg, Sebastian and Zoss, Gaspard and Chandran, Prashanth and Gotardo, Paulo and Bradley, Derek},
 title = {Facial Hair Tracking for High Fidelity Performance Capture},
 year = {2022},
 issue_date = {July 2022},
 publisher = {Association for Computing Machinery},
 address = {New York, NY, USA},
 volume = {41},
 number = {4},
 issn = {0730-0301},
 url = {https://doi.org/10.1145/3528223.3530116},
 doi = {10.1145/3528223.3530116},
 journal = {ACM Trans. Graph.},
 month = {jul},
 articleno = {165},
 numpages = {12},
 keywords = {hair tracking, facial hair capture, performance capture}
 }

Local Anatomically – Constrained Facial Performance Retargeting

Published in Siggraph, 2022

We present a new method for high-fidelity offline facial performance retargeting that is neither expensive nor artifact-prone. [Project Page]

Cite

 @article{10.1145/3528223.3530114,
 author = {Chandran, Prashanth and Ciccone, Loiic and Gross, Markus and Bradley, Derek},
 title = {Local Anatomically-Constrained Facial Performance Retargeting},
 year = {2022},
 issue_date = {July 2022},
 publisher = {Association for Computing Machinery},
 address = {New York, NY, USA},
 volume = {41},
 number = {4},
 issn = {0730-0301},
 url = {https://doi.org/10.1145/3528223.3530114},
 doi = {10.1145/3528223.3530114},
 journal = {ACM Trans. Graph.},
 month = {jul},
 articleno = {168},
 numpages = {14},
 keywords = {facial animation, expression transfer, facial performance retargeting}
 }

Improved Lighting Models for Facial Appearance Capture

Published in Eurographics, 2022

We compare the results obtained with a state-of-the-art appearance capture method, with and without our proposed improvements to the lighting model. [Project Page]’

Cite

 @inproceedings {10.2312:egs.20221019,
 booktitle = {Eurographics 2022 - Short Papers}, 
 editor = {Pelechano, Nuria and Vanderhaeghe, David}, 
 title = {{Improved Lighting Models for Facial Appearance Capture}}, 
 author = {Xu, Yingyan and Riviere, Jérémy and Zoss, Gaspard and Chandran, Prashanth and 
 Bradley, Derek and Gotardo, Paulo}, 
 year = {2022}, 
 publisher = {The Eurographics Association}, 
 ISSN = {1017-4656}, 
 ISBN = {978-3-03868-169-4}, 
 DOI = {10.2312/egs.20221019} 
 }

Shape Transformers: Topology-Independent 3D Shape Models Using Transformers

Published in Eurographics, 2022

We present a new nonlinear parametric 3D shape model based on transformer architectures. [Project Page]

Cite

 @article{https://doi.org/10.1111/cgf.14468, 
 author = {Chandran, Prashanth and Zoss, Gaspard and Gross, Markus and Gotardo, Paulo and Bradley, Derek},
 title = {Shape Transformers: Topology-Independent 3D Shape Models Using Transformers}, journal = {Computer Graphics Forum},
 volume = {41},
 number = {2},
 pages = {195-207},
 doi = {https://doi.org/10.1111/cgf.14468},
 url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14468},
 year = {2022}
 }

Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering

Published in ACM SIGGRAPH Asia, 2021

We propose to combine incomplete, high-quality renderings showing only facial skin with recent methods for neural rendering of faces, in order to automatically and seamlessly create photo-realistic full-head portrait renders from captured data without the need for artist intervention. [Project Page]

Cite

 @article{10.1145/3478513.3480509, 
 author = {Chandran, Prashanth and Winberg, Sebastian and Zoss, Gaspard and Riviere, Jeremy and Gross, Markus and Gotardo, Paulo and Bradley, Derek},
 title = {Rendering with Style: Combining Traditional and Neural Approaches for High-Quality Face Rendering},
 year = {2021},
 issue_date = {December 2021},
 publisher = {Association for Computing Machinery},
 address = {New York, NY, USA},
 volume = {40},
 number = {6},
 issn = {0730-0301},
 url = {https://doi.org/10.1145/3478513.3480509},
 doi = {10.1145/3478513.3480509},
 journal = {ACM Trans. Graph.},
 month = {dec},
 articleno = {223},
 }

Adaptive Convolutions for Structure-Aware Style Transfer

Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

We propose Adaptive convolutions; a generic extension of AdaIN, which allows for the simultaneous transfer of both statistical and structural styles in real time. [Project Page]

Cite

 @InProceedings{Chandran_2021_CVPR,
 author    = {Chandran, Prashanth and Zoss, Gaspard and Gotardo, Paulo and Gross, Markus and Bradley, Derek},
 title     = {Adaptive Convolutions for Structure-Aware Style Transfer},
 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 month     = {June},
 year      = {2021},
 pages     = {7972-7981}
 }

Semantic Deep Face Models

Published in 3D International Conference on 3D Vision (3DV), 2020

We present a method for nonlinear 3D face modeling using neural architectures. [Project Page]

Cite

 @INPROCEEDINGS {9320344,
 author = {P. Chandran and D. Bradley and M. Gross and T. Beeler},
 booktitle = {2020 International Conference on 3D Vision (3DV)},
 title = {Semantic Deep Face Models},
 year = {2020},
 pages = {345-354},
 doi = {10.1109/3DV50981.2020.00044},
 url = {https://doi.ieeecomputersociety.org/10.1109/3DV50981.2020.00044},
 publisher = {IEEE Computer Society},
 address = {Los Alamitos, CA, USA},
 month = {nov}
 }

Attention-Driven Cropping for Very High Resolution Facial Landmark Detection

Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today. [Project Page]

Cite

 @InProceedings{Chandran_2020_CVPR,
 author = {Chandran, Prashanth and Bradley, Derek and Gross, Markus and Beeler, Thabo},
 title = {Attention-Driven Cropping for Very High Resolution Facial Landmark Detection},
 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 month = {June},
 year = {2020}
 }

Road tracking using particle filters for Advanced Driver Assistance Systems

Published in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014

Road segmentation and tracking is of prime importance in Advanced Driver Assistance Systems (ADAS) to either assist autonomous navigation or provide useful information to drivers operating semi-autonomous vehicles. The work reported herein describes a novel algorithm based on particle filters for segmenting and tracking the edges of roads in real world scenarios. [Paper]

Cite

 @INPROCEEDINGS{6957884, 
 author={Chandran, Prashanth and John, Mala and Santhosh Kumar S and Mithilesh N S R}, 
 booktitle={17th International IEEE Conference on Intelligent Transportation Systems (ITSC)}, 
 title={Road tracking using particle filters for Advanced Driver Assistance Systems}, 
 year={2014}, 
 pages={1408-1414}, 
 doi={10.1109/ITSC.2014.6957884} 
 }

Segmentation and grading of diabetic retinopathic exudates using error-boost feature selection method

Published in 2011 World Congress on Information and Communication Technologies, 2011

This paper proposes a method to segment the exudates and lesions in retinal fundus images and classify using selective brightness feature. [Paper]

Cite

 @INPROCEEDINGS{6141299,  
 author={Pradeep Kumar, A.V. and Prashanth, C. and Kavitha, G.}, 
 booktitle={2011 World Congress on Information and Communication Technologies},  
 title={Segmentation and grading of diabetic retinopathic exudates using error-boost feature selection method}, 
 year={2011}, 
 pages={518-523}, 
 doi={10.1109/WICT.2011.6141299} 
 }