Aniruddha Mahapatra
I am a sophomore 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 Aliaksandr Siarohin, Hsin-Ying Lee
and Sergey Tulyakov at Snap Research, 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.
Email  | 
Resume  | 
Google Scholar  | 
LinkedIn
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CMU MS in Computer Vision Aug. 22 - Dec. 23
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Snap Research Intern May. 23 - Aug. 23
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Adobe Research Associate Aug. 20 - Present
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Adobe Research Intern May. 19 - Jul. 19
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IBM Remote Research Intern Jun. 18 - Dec. 18
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IIT Roorkee
B.Tech. Computer Science
Jul. 16 - Jul. 20
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Text-Guided Synthesis of Eulerian Cinemagraphs
Aniruddha Mahapatra,
Aliaksandr Siarohin,
Hsin-Ying Lee,
Sergey Tulyakov,
Jun-Yan Zhu
SIGGRAPH Asia, 2023 (ACM Transactions on Graphics)
webpage |
paper |
arXiv |
code |
bibTeX
We introduce a fully automated method, Text2Cinemagraph, for creating cinemagraphs from text descriptions -
an especially challenging task when prompts feature imaginary elements and artistic styles,
given the complexity of interpreting the semantics and motions of these images. Our method also gives the user a coarse control over the direction of motion in the generated cinemagraphs using text-based direction.
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Controllable Animation of Fluid Elements in Still Images
Aniruddha Mahapatra,
Kuldeep Kulkarni
CVPR, 2022
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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.
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GEMS: Scene Expansion using Generative Models of Graphs
Aniruddha Mahapatra*,
Rishi Agarwal*,
Tirupati Saketh Chandra*,
Vaidehi Patil*,
Kuldeep Kulkarni,
Vishwa Vinay
WACV, 2023
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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.
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Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models
Aniruddha Mahapatra,
Snarmila Nangi,
Aparna Garimella,
Anandhavelu Natarajan
EMNLP, 2022 (Oral)
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bibTeX
We introduce effective ways of dataset selection for pretraining large language models in an unsupervised way to facilitate domian adaptation to very limited data domains.
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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.
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Unsupervised Domain Adaptation for Remote Sensing Images Using Metric Learning and Correlation Alignment
Aniruddha Mahapatra,
Biplab Banerjee
NCVPRIPG 2019, 2021 (Oral)
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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.
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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
IEEE IGARSS, 2019
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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.
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