no code implementations • CVPR 2023 • Fabrizio Guillaro, Davide Cozzolino, Avneesh Sud, Nicholas Dufour, Luisa Verdoliva
In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning.
Ranked #1 on Image Manipulation Detection on Columbia
1 code implementation • 26 Jul 2022 • Reuben Tan, Bryan A. Plummer, Kate Saenko, JP Lewis, Avneesh Sud, Thomas Leung
Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images.
no code implementations • 21 Oct 2021 • Kyle Genova, Xiaoqi Yin, Abhijit Kundu, Caroline Pantofaru, Forrester Cole, Avneesh Sud, Brian Brewington, Brian Shucker, Thomas Funkhouser
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.
Ranked #8 on LIDAR Semantic Segmentation on nuScenes
no code implementations • ICCV 2021 • Forrester Cole, Kyle Genova, Avneesh Sud, Daniel Vlasic, Zhoutong Zhang
We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement.
1 code implementation • CVPR 2022 • Ben Usman, Andrea Tagliasacchi, Kate Saenko, Avneesh Sud
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date.
Ranked #1 on 3D Human Pose Estimation on SkiPose
no code implementations • 17 Jun 2021 • Bo Hu, Bryan Seybold, Shan Yang, David Ross, Avneesh Sud, Graham Ruby, Yi Liu
We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos.
no code implementations • 18 Dec 2020 • Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi
We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint.
no code implementations • 9 Nov 2020 • Fangyin Wei, Elena Sizikova, Avneesh Sud, Szymon Rusinkiewicz, Thomas Funkhouser
Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e. g., the pose of a person's arm or the length of an airplane's wing) while preserving as much existing details as possible.
1 code implementation • NeurIPS 2020 • Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko
We show that, under certain assumptions, this combination yields a deep neural likelihood-based minimization objective that attains a known lower bound upon convergence.
1 code implementation • 19 Mar 2020 • Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
1 code implementation • CVPR 2020 • Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
no code implementations • 7 Jun 2019 • Jake Levinson, Avneesh Sud, Ameesh Makadia
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR.
1 code implementation • 6 Dec 2018 • Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia
This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
no code implementations • 22 Jul 2017 • Steven Hickson, Nick Dufour, Avneesh Sud, Vivek Kwatra, Irfan Essa
One of the main challenges of social interaction in virtual reality settings is that head-mounted displays occlude a large portion of the face, blocking facial expressions and thereby restricting social engagement cues among users.