Ashwath Shetty

I'm a Research Assistant at the Computer Vision and Machine Learning Department, at the Max Planck Insitute working with Jonas Fischer on understanding the effect of sparsity on network intepretibility .

Before that, I did my Masters Thesis student at the Visual Computing and AI Department, at the Max Planck Insitute in Saarbrücken. I did my thesis working on Real time photorealistic telepresence with Marc Habermann and Christian Theobalt. Previosly I worked as Research Assistant under Avinash Sharma on 3D Performance Capture at the CVIT Lab in IIIT Hyderabad. I got my Bachelor degree from beautiful MIT Manipal Karnataka, and did my bachelors thesis at IIT Bombay under Ganesh Ramakrishnan.

Hobbies and free time activities:Running, Meditation, Hiking, Martial Arts, Wind instruments (Bamboo Flute and Harmonica), Irish Dancing, Chess (I'm just average in all of them :))

Email  /  CV  /  DBLP  /  Github

profile photo

Research

I'm primarily interested in computer vision, machine learning, optimization, geometry processing and the intersection of these fields. Most of my previous research has covered diverse aspects of modelling humans in motion in a photorealistic and efficient way, however I am eager to learn more about robust representation learning, interpretibility, and neuroscience. If you have any projects where you think I could be useful or could learn from, feel free to reach out.

Holoported Charcters
Ashwath Shetty Marc Habermann, Guoxin Sun, Diogo Luvizon, Vladislav Golyanik, Christian Theobalt (Project Page) CVPR, 2024

Proposed a method for Real time 4K free viewpoint rendering from four RGB cameras (Thesis)

End-to-End Learnable Masks With Differentiable Indexing
Ashwath Shetty, Sree Harsha Nelaturu Dibyanshu Shekhar Ilia Sucholotsky Tiny Papers @ ICLR, 2023

Proposed a diffrentiable masking mechanism, that can be integrated with learning pipelines

Attention based Occlusion Removal for Hybrid Telepresence Systems
Surabhi Gupta Ashwath Shetty, Avinash Sharma, CRV, 2022

Proposed a method, for deocclusion for Head Mounted Displays, and integration with Virtual Reality

Selected Experience (See CV for more)

Research Assistant at the CVML Department, Max Planck Insitute for Informatics ( supervised by Jonas Fischer) (March 2024 -Present)


  • Sparse Compositional interpretibility Understanding the link between sparsity and interpretability, and using a sparse network to read off a networks decision making process

Research Assistant at the VCAI Department, Max Planck Insitute for Informatics ( supervised by Marc Habermann and Christian Theobalt) (July 2022-Jan 2024)


  • Holoported Characters Built an animatable human representation, that takes an sparse images and 3D pose, and can render humans in arbitrary viewpoints at 4K resolution (Selected as one of the inaugaral VIA centre projects)

Research Assistant at the Centre for Visual Information Technology, IIIT Hyderabad ( supervised by Avinash Sharma) (July 2020-Jan 2022)


  • 3D SMPL+D Registration Setup a multi stage pipeline for fitting SMPL followed by SMPL+D to a 3D human scan. (Sample Results)
  • Face Deocclusion Research on Face Deocclusion and Reconstruction, from videos
  • Some other cool projects Data generation pipeline from stylgan and styleflow (Sample Results), Event Camera based SMPL Tracking

Research Intern at DreamVu ( supervised by Avinash Sharma and Anoop Namboodiri) (July 2022-Present)


  • 3D Dance Capture Worked on TSDF fusion mesh details, with deep learning models for faces (Sample Results)

Online Courses and Summer Schools

I enjoy learning, so I do spend a lot of my time doing online courses in random and sometimes relevant topics.

Certified: (Link to Certificates) Algorithms (Stanford), Probabilistic Graphical Models (Stanford), Deep Learning (deeplearning.ai), Tensorflow in Practice (deeplearning.ai), Machine Learning(Stanford), Advanced Machine Learning (HSE), Reinforcement Learning (UAlberta), Programming Languages (UWash)

Online Audit: Calculus (MIT), Math for CS (MIT), Linear Algebra (MIT), CNNs for Vision (Stanford), NLP with Deep Learning (Stanford), Learning from Data (Caltech), Interactive Computer Graphics (Udacity), Computer Vision (Udacity\&Gtech), Practical DL for coders (fast.ai), Deep Learning (NYU)

Summer Schools: Easter European Machine Learning, ACM Graph Theory and Graph Algorithms

I feel with the amount of information out there, anyone with a laptop and good interent connection can learn whatever they want to, I owe a lot of skillset to online courses and material, and one of my goals is to teach and contribute to a MOOC (If I can help in any way please reach out)

Selected Projects (Refer to CV for more)

Ray Tracing Engine: A fully functional ray tracer, which produced the image attached. (Winners of Rendering Competition 2021)
Poisson Mesh Editing: Implementation of Poisson Mesh Editing with interactive tool
Lips don't Lie : Fooling and defending against attacks on Lip Reading Models
On the Effect of Input Transformations to Adversarial Robustness
Semi Supervised Learning Implementations, and an improvement to FixMatch using a VQVAE

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