no code implementations • 21 Mar 2024 • Xiang Fan, Anand Bhattad, Ranjay Krishna
We introduce Videoshop, a training-free video editing algorithm for localized semantic edits.
no code implementations • 28 Nov 2023 • Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra, Svetlana Lazebnik, D. A. Forsyth, Anand Bhattad
All three classifiers are denied access to image pixels, and look only at derived geometric features.
no code implementations • 28 Nov 2023 • Xiaodan Du, Nicholas Kolkin, Greg Shakhnarovich, Anand Bhattad
Generative models have been shown to be capable of synthesizing highly detailed and realistic images.
1 code implementation • ICCV 2023 • Yuanyi Zhong, Anand Bhattad, Yu-Xiong Wang, David Forsyth
Dense depth and surface normal predictors should possess the equivariant property to cropping-and-resizing -- cropping the input image should result in cropping the same output image.
no code implementations • 7 Jul 2023 • Vaibhav Vavilala, Seemandhar Jain, Rahul Vasanth, Anand Bhattad, David Forsyth
We present Blocks2World, a novel method for 3D scene rendering and editing that leverages a two-step process: convex decomposition of images and conditioned synthesis.
no code implementations • 15 Jun 2023 • Zhi-Hao Lin, Bohan Liu, Yi-Ting Chen, David Forsyth, Jia-Bin Huang, Anand Bhattad, Shenlong Wang
UrbanIR uses a novel loss to make very good estimates of shadow volumes in the original scene.
no code implementations • 27 Apr 2023 • Anand Bhattad, Viraj Shah, Derek Hoiem, D. A. Forsyth
StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge.
no code implementations • 20 May 2022 • Anand Bhattad, D. A. Forsyth
We propose a novel method, StyLitGAN, for relighting and resurfacing generated images in the absence of labeled data.
no code implementations • 8 Dec 2021 • D. A. Forsyth, Anand Bhattad, Pranav Asthana, Yuanyi Zhong, YuXiong Wang
Novel theory shows that one can use similar scenes to estimate the different lightings that apply to a given scene, with bounded expected error.
2 code implementations • CVPR 2022 • Liwen Wu, Jae Yong Lee, Anand Bhattad, YuXiong Wang, David Forsyth
DIVeR's representation is a voxel based field of features.
no code implementations • CVPR 2021 • Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro
We describe a cycle consistency loss that encourages model textures to be aligned, so as to encourage sharing.
no code implementations • 12 Oct 2020 • Anand Bhattad, David A. Forsyth
We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene.
1 code implementation • 21 Oct 2019 • Mao-Chuang Yeh, Shuai Tang, Anand Bhattad, Chuhang Zou, David Forsyth
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image.
1 code implementation • ICLR 2020 • Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, D. A. Forsyth
Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation.
no code implementations • 31 Mar 2018 • Mao-Chuang Yeh, Shuai Tang, Anand Bhattad, D. A. Forsyth
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image.
no code implementations • 15 Feb 2018 • Anand Bhattad, Jason Rock, David Forsyth
We describe a method for detecting an anomalous face image that meets these requirements.