Aniruddha Mahapatra

I am a first year Masters in Computer Vision (MSCV) student at Carnegie Mellon University. I am advised by Prof. Jun-Yan Zhu. My research interest include computer vision and deep learning, specifically, image and video synthesis and editing using generative models. My goal is to create automated AI algorithms for generating (or editing) photorealistic images and videos which are very time-consuming, or otherwise impossible to do manually by existing tools.

I am fortunate to have worked with Kuldeep Kulkarni, Anandhavelu Natarajan and Subrata Mitra at Adobe Research, Jitendra Singh at IBM Research, and, Professor Biplab Banerjee and Ranita Biswas at Indian Institute of Technology, Roorkee.

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MS in Computer Vision
Aug. 22 - Dec. 23


Research Associate
Aug. 20 - Present


Research Intern
May. 19 - Jul. 19


Remote Research Intern
Jun. 18 - Dec. 18


IIT Roorkee
B.Tech. Computer Science
Jul. 16 - Jul. 20

GEMS: Scene Expansion using Generative Models of Graphs
Aniruddha Mahapatra*, Rishi Agarwal*, Tirupati Saketh Chandra*, Vaidehi Patil*, Kuldeep Kulkarni, Vishwa Vinay
WACV, 2023
paper | arXiv | bibTeX

We design an auto-regressive model, GEMS, for a novel task of conditional expansion of scene graphs from a given seed scene graph that adds nodes and edges hierarchically to the seed scene graph. We also propose novel metrics to evaluate the quality of expanded scene graphs to capture the coherence of predicted edges and nodes better than traditional MMD based metrics.

Controllable Animation of Fluid Elements in Still Images
Aniruddha Mahapatra, Kuldeep Kulkarni
CVPR, 2022
webpage | paper | arXiv | video | bibTeX

Given a single input image, mask the region user wants to animate and any number of arrow directions and their associated speeds provided by the user to specify the direction of desired movement, we propose a method to interactively control the animation of fluid elements (like water, fire, clouds, etc.) to generate cinemagraphs from the single image.

SemIE: Semantically-aware Image Extrapolation
Bholeshwar Khurana, Soumya Ranjan Dash, Abhishek Bhatia, Aniruddha Mahapatra, Hrituraj Singh, Kuldeep Kulkarni
ICCV, 2021
webpage | paper | arXiv | bibTeX | video (infinite zooming-out)

We propose a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances.

Unsupervised Domain Adaptation for Remote Sensing Images Using Metric Learning and Correlation Alignment
Aniruddha Mahapatra, Biplab Banerjee
NCVPRIPG 2019, 2021 (Oral Presentation)
paper | bibTeX

We prose an end-to-end trainable neural network-based unsupervised DA module for RS image classification that learns a shared embedding space for both the domains which are also deemed to be discriminative by jointly optimizing the contrastive loss and minimizing the difference of the two domain higher-order statistics.

Assessment of Sentinel-1 and Sentinel-2 Satellite Imagery for Crop Classification in Indian Region During Kharif and Rabi Crop Cycles
Jitendra Singh, Aniruddha Mahapatra, Saurav Basu, Biplab Banerjee
paper | bibTeX

We evaluate the potential of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery in crop classification for an Indian region using multi-class classification algorithm based on the support vector machine (SVM) by applying it to the temporal features extracted from the two imagery data.

Teaching Experience
Awards and Scholarships
  • Class Rank 2 | Ranked 2nd out of 76 students in IIT Roorkee B.Tech. Computer Science batch of 2020.
  • Rai Singh Jain & Mrs. Shakuntla Devi Jain Scholarship | Awarded by IIT Roorkee for the best academic performance among all B.Tech. CSE/ECE/Elec. programs up to 1st Year.
  • 1988 Batch Award | Awarded by IIT Roorkee for the best academic performance among all UG programs up to 1st semester.
  • 1988 Batch Award | Awarded by IIT Roorkee for the best academic performance among all UG programs up to 1st year.
  • KVPY Fellowship | National Fellowship awarded by Indian Institute of Science and Govt. of INDIA.

Source code from Jon Barron