Anand Bhattad

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

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I am a PhD student in the Computer Vision Group at UIUC working with Prof. David A. Forsyth. 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

Get in touch

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

Reach out to me if you are interested in collaborating on a project. See my research goals and recent papers below if you are interested in my work. I always look forward to and enjoy working with junior students and other collaborators.

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

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.