Shashank Tripathi

I am a PhD student (2021-) at the Max Planck Institute for Intelligent Systems where I am advised by MPI Director Michael Black. Earlier, I worked as an Applied Scientist at Amazon (2019-2021). I earned my Masters (2017-2019) from the Robotics Institute, Carnegie Mellon University, working with Prof. Kris Kitani

At Amazon Lab126, I closely collaborated with Prof. James Rehg, Dr. Amit Agrawal and Dr. Ambrish Tyagi. It has been my great fortune to have worked with excellent mentors and advisors.

Email  /  Google Scholar  /  GitHub /  LinkedIn / Twitter / CV

Publications / Patents / Misc


My research lies at the intesection of machine-learning, computer vision and computer graphics. Specifically, I am interested in 3D modelling of human bodies with limited supervision. I am also working on understanding human motion in 3D. In the past, I have worked on synthetic data for applications like object detection and human pose estimation.

Before diving into human body research, I dabbled in visual-servoing, medical-image analysis, pedestrian-detection and reinforcement learning.


3D Human Pose Estimation via Intuitive Physics
Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael Black, Dimitrios Tzionas
Computer Vision and Pattern Recognition (CVPR) 2023

IPMAN estimates a 3D body from a color image in a "stable" configuration by encouraging plausible floor contact and overlapping CoP and CoM. It exploits interpenetration of the body mesh with the ground plane as a heuristic for pressure.

abstract | bibtex

Occluded Human Mesh Recovery
Rawal Khirodkar, Shashank Tripathi, Kris Kitani
Computer Vision and Pattern Recognition (CVPR) 2022

A novel top-down mesh recovery architecture capable of leveraging image spatial context for handling multi-person occlusion and crowding.

paper | abstract | project |

AGORA: Avatars in Geography Optimized for Regression Analysis
Priyanka Patel, Chun-Hao P. Huang, Joachim Tesch, David T. Hoffman, Shashank Tripathi and Michael J. Black
Computer Vision and Pattern Recognition (CVPR) 2021

A synthetic dataset with high realism and highly accurate ground truth containing 4240 textured scans and SMPLX fits.

paper | abstract | project | video

PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation
Shashank Tripathi, Siddhant Ranade, Ambrish Tyagi and Amit Agrawal
International Conference on 3D Vision (3DV), 2020 (oral presentation)

Temporally consistent recovery of 3D human pose from 2D joints without using 3D data in any form

paper | abstract | videos

Learning to Generate Synthetic Data via Compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal, Ambrish Tyagi, James Rehg and Visesh Chari
Computer Vision and Pattern Recognition (CVPR) 2019

Efficient, task-aware and realisitic synthesis of composite images for training classification and object detection models

paper | abstract | poster

C2F: Coarse-to-Fine Vision Control System for Automated Microassembly
Shashank Tripathi, Devesh Jain and Himanshu Dutt Sharma
Nanotechnology and Nanoscience-Asia 2018

Automated, visual-servoing based closed loop system to perform 3D micromanipulation and microassembly tasks

paper | abstract | video

Sub-cortical Shape Morphology and Voxel-based Features for Alzheimer's Disease Classification
Shashank Tripathi, Seyed Hossein Nozadi, Mahsa Shakeri and Samuel Kadoury
IEEE International Symposium on Biomedical Imaging (ISBI) 2017

Alzheimer's disease patient classification using a combination of grey-matter voxel-based intensity variations and 3D structural (shape) features extracted from MRI brain scans

paper | abstract | poster

Deep Spectral-Based Shape Features for Alzheimer’s Disease Classification
Mahsa Shakeri, Hervé Lombaert, Shashank Tripathi and Samuel Kadoury
MICCAI Spectral and Shape Analysis in Medical Imaging (SeSAMI) 2016

Alzheimer's disease classification by using deep learning variational auto-encoder on shape based features

paper | abstract


Generation of synthetic image data using three-dimensional models

Generation of synthetic image data for computer vision models


Some other unpublished work:

Learning Salient Objects in a Scene using Superpixel-augmented Convolutional Neural Networks

Moving object detection, tracking and classification from an unsteady camera

Towards integrating model dynamics for sample efficient reinforcement learning

adapted from Jon Barron's awesome webpage