Anand Bhattad

Research Assistant Professor
Toyota Technological Institute at Chicago

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Top 3 Research Highlights: ppt, pdf

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I am a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC) located in the University of Chicago campus. At TTIC, I actively collaborate with PALS. Earlier, I completed my PhD from Computer Vision Group at University of Illinois Urbana-Champaign (UIUC) with my advisor David Forsyth. During my PhD, I had the pleasure of collaborating with Derek Hoiem, Shenlong Wang, and Yuxiong Wang at UIUC.


My research is centered around the fields of computer vision, computer graphics, computational photography, and machine learning. I work on developing methods that are grounded in real-world physics to better understand, model, and recreate what we see around us. At the moment, my focus is on generative models to explore the kind of knowledge they capture and their limitations. My goal is to recover inherent scene characteristics these models learn and apply them to build advanced computer vision systems capable of creating and modeling digital twins.

I am also interested in leveraging generative models for neural rendering and 3D-aware image editing. This aims to provide easy-to-use tools for interacting with and manipulating visual content.

  • Mar 2024: Teaching a new course: Past Meets Present: A Tale of Two Visions at TTIC this Spring.
  • Feb 2024: Organzing CV 20/20: A Retrospective Vision workshop at CVPR 2024.
  • Feb 2024: Congratulations to James Soole, Ayush Sarkar, Asher Mai and Amitabh Mahapatra on their CVPR 2024 papers!
  • Sept 2023: Congratulations to Oscar Michel on his NeurIPS 2023 paper acceptance!
  • Sept 2023: Excited to start as a Research Assistant Professor at TTIC!
  • July 2023: One paper accepted by ICCV 2023 on Equivariant Dense Prediction Models.
  • Jun 2023: Organized Scholars & Big Models: How Can Academics Adapt? workshop at CVPR 2023.
  • May 2023: Successfully defended my PhD thesis titled "Exploring Knowledge in Generative Models"!
Recent and Upcoming Talks
  • IIIT-Hyderabad, Jan 2024
  • TTIC, Oct 2023
  • Exactech, Inc., Oct 2023
  • Stanford University, Jun 2023
  • University of Tübingen, Autonomous Vision Group, May 2023
  • 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
Recent Awards
  • Outstanding Reviewer Award, ICCV 2023
  • Best Paper Finalist, CVPR 2022
  • Outstanding Emergency Reviewer Award, CVPR 2021


Xiaodan Du, Nicholas Kolkin, Greg Shakhnarovich, Anand Bhattad
arXiv, 2023
[arXiv] [project page] [code]

Intrinsic LoRA: A method to extract intrinsic images from ANY generative model, be it Autoregressive, GAN, Diffusion!

Zhi-Hao Lin, Bohan Liu, Yi-Ting Chen, David Forsyth, Jia-Bin Huang, Anand Bhattad, Shenlong Wang
arXiv, 2023
[arXiv] [project page]

UrbanIR creates realistic 3D renderings of urban scenes from single videos, allowing for novel lighting conditions and controllable editing.

Vaibhav Vavilala, Seemandhar Jain*, Rahul Vasanth*, Anand Bhattad, David Forsyth
arXiv, 2023

Blocks2World decomposes 3D scenes into editable primitives and uses a trained model to render these into 2D images, providing high control for scene editing.

Anand Bhattad, Viraj Shah Derek Hoiem, David A. Forsyth
arXiv, 2023
[arXiv], [project page]

A novel near-perfect GAN Inversion method that preserves editing capabilities, even for out-of-domain images

David A. Forsyth, Anand Bhattad, Pranav Asthana, Yuani Zhong, Yuxiong Wang
arXiv, 2022
Technical Report

First scene relighting method that requires no labeled or paired image data.


Ayush Sarkar*, Hanlin Mai*, Amitabh Mahapatra*, Svetlana Lazebnik, David Forsyth, Anand Bhattad
CVPR, 2024
[arXiv] [project page]

Generative models are not aware of projective geometry. We show that generated images can be easily distinguished by looking at derived projective geometry cues.

Anand Bhattad, James Soole, David A. Forsyth
CVPR, 2024
[paper] [arXiv] [project page] [code]

By imposing known physical facts about images, we can prompt StyleGAN to generate relighted or resurfaced images without using labeled data.


Anand Bhattad, Daniel McKee, Derek Hoiem, David Forsyth
NeurIPS, 2023

StyleGAN has easy accssible internal encoding of intrinsic images as originally defined by Barrow and Tenenbaum in their influential paper of 1978.

Oscar Michel, Anand Bhattad, Eli VanderBilt, Ranjay Krishna, Ani Kembhavi, Tanmay Gupta
NeurIPS, 2023
[arXiv] [project page]

A synthetic dataset and a model that learns to rotate, translate, insert, and remove objects identified by language in a scene. It can transfer to real-world images.

Yuani Zhong, Anand Bhattad, Yuxiong Wang David A. Forsyth
ICCV 2023

SOTA normal and depth predictors are not equivariant to image cropping. We propose equivariant regularization loss to improve equivariance in these models.

Anand Bhattad, David A. Forsyth
3DV, 2022
[project page]

Convincing cut-and-paste reshading with consistent image decomposition inferences.

Liwen Wu, Jae Yong Lee, Anand Bhattad, Yuxiong Wang, David A. Forsyth
CVPR, 2022 (Best Paper Finalist)
[project page] / Training Code / Real-time Code

Improving Real-Time NeRF with Deterministic Integration.

Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro
CVPR, 2021
[project page]

Consistent textured 3D inferences from a single 2D image.

Anand Bhattad*, Min Jin Chong*, Kaizhao Liang, Bo Li, David A. Forsyth
ICLR, 2020

Generating realistic adversarial examples by image re-colorization and texture transfer.

Mao Chuang Yeh*, Shuai Tang*, Anand Bhattad, Chuhang Zou, David A. Forsyth
WACV, 2020

A novel quantitative evaluation procedure for style transfer methods.


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.


Course Instructor, TTIC 41000 Past Meets Present: A Tale of Two Visions, Spring 2024

Teaching Assistant, CS498 Applied Machine Learning, Fall 2018

Teaching Assistant, CS225 Data Structures, Spring 2017

Teaching Assistant, CS101 Intro Computer Science Fall 2017

Teaching Assistant, CS101 Intro Computer Science, Spring 2016 (Rated as Outstanding TA)

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