Anand Bhattad

PhD Student
Advisor: David A. Forsyth
Computer Science Department
University of Illinois Urbana Champaign

Email / CV / Google Scholar / Twitter / Github

I am a PhD student in the Computer Vision Group at UIUC working with Prof. David A. Forsyth. I also closely collaborate with Derek Hoiem, Shenlong Wang and Yuxiong Wang. My research interests are in computer vision and computational photography.

Earlier, I graduated from UIUC with an MS in Computer Science and fell in love with computer vision. In my previous life, I enjoyed working on the Damage Assessment of Civil Infrastructure Systems. During those times, I graduated from UIUC with an MS and NITK Surathal in India with a BTech in Civil Engineering.

During my PhD, I have had the pleasure of working with

Ani Kembhavi at Allen Institute of AI in their PRIOR team (present)
Stephan Richter at Intel in 2021 in Vladlen Koltun's Intelligent Systems Lab
Aysegul Dundar, Guilin Liu, Andrew Tao and Bryan Catanzaro in 2020 at NVIDIA's Applied Deep Learning Research team
Abhishek Kar and Rodrigo Ortiz Cayón in 2019 at Fyusion Inc.

Get in touch

I am interested in an academic career and currently seeking a postdoc position starting Fall 2023. Please email me if you think I would be a good fit for your team.

I organize computer vision speaker series at UIUC. If you are interested in giving a talk at UIUC, please email me.

Recent and Upcoming Talks

UC Berkeley: Vision Seminar, Apr 2023
NVIDIA Research, Apr 2023
MIT: Vision and Graphics Seminar, Apr 2023
CMU: VASC Seminar, Mar 2023
UW: Vision Seminar, Mar 2023
UMD: Vision Seminar, Mar 2023
UCSD: Pixel Cafe Seminar, Feb 2023
TTIC: Research Talk, Feb 2023

Affiliations


NITK Surathkal 2011-2015	MSU Summer 2014	UIUC 2015-Present	Siemens Summer 2017	Fyusion Inc Summer 2019	NVIDIA Summer & Fall 2020	Intel Spring & Summer 2021	AI2 Spring 2023

Research

The primary aim of my research is easy and fast image editing. I create algorithms that can convincingly insert objects cut from one or more images and pasted into another, relight single images or generate objects and scenes from novel views. The goal is to go beyond this. One should be able to perform all these tasks using a single framework with ease.

In the long-term, I see a neural rendering engine taking over physically based rendering engines. My research focuses on making this vision a reality.

Publications

	Enriching StyleGAN with Illumination Physics Anand Bhattad, David A. Forsyth arXiv, 2022 arXiv We show that by imposing known physical facts about images, the distribution of images produced by a StyleGAN can be significantly enriched.
	Image-based Object Insertion using Persistent and Transient Decomposition Anand Bhattad, Brian Chen, Stephan R. Richter, David A. Forsyth arXiv, 2022 Coming Soon First self-supervised, image-based object relighting method trained without labeled paired data, CGI data, geometry, or environment maps.
	SIRfyN: Single Image Relighting from your Neighbors David A. Forsyth, Anand Bhattad, Pranav Asthana, Yuani Zhong, Yuxiong Wang arXiv, 2022 Early Draft First scene relighting method that requires no labeled or paired image data.
	Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading Anand Bhattad, David A. Forsyth Internation Conference on 3D Vision (3DV), 2022 Project Convincing cut-and-paste reshading with consistent image decomposition inferences.
	DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering Liwen Wu, Jae Yong Lee, Anand Bhattad, Yuxiong Wang, David A. Forsyth Computer Vision and Pattern Recognition (CVPR), 2022 (Best Paper Finalist) Project / Training Code / Real-time Code Improving Real-Time NeRF with Deterministic Integration.
	View Generalization for Single Image Textured 3D Models Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro Computer Vision and Pattern Recognition (CVPR), 2021 Project Consistent textured 3D inferences from a single 2D image.
	Unrestricted Adversarial Examples via Semantic Manipulation Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, David A. Forsyth International Conference on Learning Representations (ICLR), 2020 CVPR-W on Adversarial ML in Real-World Computer Vision Systems, 2019 Generating realistic adversarial examples by image re-colorization and texture transfer.
	Improved Style Transfer with Calibrated Metrics Mao Chuang Yeh, Shuai Tang, Anand Bhattad, Chuhang Zou, David A. Forsyth Winter Conference on Applications of Computer Vision (WACV), 2020 A novel quantitative evaluation procedure for style transfer methods.
	Detecting Anomalous Faces with "No Peeking'' Autoencoders Anand Bhattad, Jason Rock, David A. Forsyth CVPR Workshop on Vision with Biased or Scarce Data, 2018 A simple unsupervised method for detecting anomalous faces by carefully constructing features from "No Peeking" or inpainting autoencoders.

Teaching

Graduate Teaching Assistant, CS498 Applied Machine Learning, Fall 2018

Graduate Teaching Assistant, CS225 Data Structures, Spring 2017

Graduate Teaching Assistant, CS101 Intro Computer Science, Spring 2016 (Ranked as Outstanding TA) & Fall 2017

Template credit: Jon Barron.