Prashanth Chandran Prashanth Chandran Prashanth chandra has 23 repositories available. follow their code on github. I am a research scientist (machine learning) at google ar vr. i enjoy working on creative applications at the intersection of computer vision, graphics, and machine learning. please take a look at some of my publications to know more about my work :).
Prashanth Chandran Prashanth Chandran Python | data structures and algorithms | machine learning | deep learning | c programming · education: marri laxman reddy institute of technology and management · location: 500002 · 39 connections. Senior associate engineer caterpillar inc. july 2013 august 2016 role: embedded electronics engineer. Prashanth chandra reddy dwarampudi prashanth chandra follow 3 followers · 11 following. Something went wrong, please refresh the page to try again. if the problem persists, check the github status page or contact support.
Prashanth Chandran Prashanth Chandran Prashanth chandra reddy dwarampudi prashanth chandra follow 3 followers · 11 following. Something went wrong, please refresh the page to try again. if the problem persists, check the github status page or contact support. To capture the expressive, detailed nature of human heads, including skin furrowing and finer scale facial movements, we propose to couple locally defined facial expressions with 3d gaussian splatting to enable creating ultra high fidelity, expressive and photorealistic head avatars. Pchandra73 has 4 repositories available. follow their code on github. @inproceedings {xu2024, author = { xu, yingyan and chandran, prashanth and weiss, sebastian and gross, markus and zoss, gaspard and bradley, derek }, booktitle = { 2024 ieee cvf conference on computer vision and pattern recognition (cvpr) }, title = {artist friendly relightable and animatable neural heads}, year = {2024}, pages = {2457 2467. In this work, we aim to make physics based facial animation more accessible by proposing a generalized physical face model that we learn from a large 3d face dataset. once trained, our model can be quickly fit to any unseen identity and produce a ready to animate physical face model automatically.
Prashanth Chandran Prashanth Chandran To capture the expressive, detailed nature of human heads, including skin furrowing and finer scale facial movements, we propose to couple locally defined facial expressions with 3d gaussian splatting to enable creating ultra high fidelity, expressive and photorealistic head avatars. Pchandra73 has 4 repositories available. follow their code on github. @inproceedings {xu2024, author = { xu, yingyan and chandran, prashanth and weiss, sebastian and gross, markus and zoss, gaspard and bradley, derek }, booktitle = { 2024 ieee cvf conference on computer vision and pattern recognition (cvpr) }, title = {artist friendly relightable and animatable neural heads}, year = {2024}, pages = {2457 2467. In this work, we aim to make physics based facial animation more accessible by proposing a generalized physical face model that we learn from a large 3d face dataset. once trained, our model can be quickly fit to any unseen identity and produce a ready to animate physical face model automatically.
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