Anand Bhattad

Preprints
	Generative Models: What do they know? Do they know things? Let's find out! 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!
	UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video 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.
	Blocks2World: Controlling Realistic Scenes with Editable Primitives Vaibhav Vavilala, Seemandhar Jain, Rahul Vasanth, Anand Bhattad, David Forsyth arXiv, 2023 arXiv Blocks2World decomposes 3D scenes into editable primitives and uses a trained model to render these into 2D images, providing high control for scene editing.
	Make It So: Steering StyleGAN for Any Image Inversion and 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
	SIRfyN: Single Image Relighting from your Neighbors 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.

2024
	Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now 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.
	StyLitGAN: Image-based Relighting via Latent Control Anand Bhattad, James Soole, David A. Forsyth CVPR, 2024 [paper] [arXiv] [project page] By imposing known physical facts about images, we can prompt StyleGAN to generate relighted or resurfaced images without using labeled data.
2023
	StyleGAN knows Normal, Depth, Albedo and more Anand Bhattad, Daniel McKee, Derek Hoiem, David Forsyth NeurIPS, 2023 [arXiv] StyleGAN has easy accssible internal encoding of intrinsic images as originally defined by Barrow and Tenenbaum in their influential paper of 1978.
	OBJect 3DIT: Language-guided 3D-aware Image Editing 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.
	Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors 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.
2022
	Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading Anand Bhattad, David A. Forsyth 3DV, 2022 [project page] 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 CVPR, 2022 (Best Paper Finalist) [project page] / Training Code / Real-time Code Improving Real-Time NeRF with Deterministic Integration.
2021
	View Generalization for Single Image Textured 3D Models Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro CVPR, 2021 [project page] Consistent textured 3D inferences from a single 2D image.
2020
	Unrestricted Adversarial Examples via Semantic Manipulation 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.
	Improved Style Transfer with Calibrated Metrics Mao Chuang Yeh, Shuai Tang, Anand Bhattad, Chuhang Zou, David A. Forsyth WACV, 2020 A novel quantitative evaluation procedure for style transfer methods.
2018
	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.