no code implementations • 13 Mar 2024 • Linyi Jin, Nilesh Kulkarni, David Fouhey
This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images.
no code implementations • 5 Mar 2024 • Chris Rockwell, Nilesh Kulkarni, Linyi Jin, Jeong Joon Park, Justin Johnson, David F. Fouhey
Estimating relative camera poses between images has been a central problem in computer vision.
no code implementations • 14 Jul 2023 • Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, Leonidas Guibas
This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics.
no code implementations • CVPR 2023 • Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey
We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data.
no code implementations • 8 Dec 2021 • Nilesh Kulkarni, Justin Johnson, David F. Fouhey
We present an approach for full 3D scene reconstruction from a single unseen image.
no code implementations • 3 May 2021 • Alexander Raistrick, Nilesh Kulkarni, David F. Fouhey
At the heart of our approach is the idea of collision replay, where we use examples of a collision to provide supervision for observations at a past frame.
no code implementations • 16 Jul 2020 • Shubham Tulsiani, Nilesh Kulkarni, Abhinav Gupta
We present an approach to infer the 3D shape, texture, and camera pose for an object from a single RGB image, using only category-level image collections with foreground masks as supervision.
1 code implementation • CVPR 2020 • Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani
We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape, and 2) inferring the articulation and pose of the template corresponding to the input image.
1 code implementation • ICCV 2019 • Nilesh Kulkarni, Abhinav Gupta, Shubham Tulsiani
We explore the task of Canonical Surface Mapping (CSM).
no code implementations • ICCV 2019 • Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, Abhinav Gupta
We propose an approach to predict the 3D shape and pose for the objects present in a scene.
1 code implementation • COLING 2018 • Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.
no code implementations • WS 2017 • Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes.
1 code implementation • 6 Jul 2017 • Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.
no code implementations • 12 May 2017 • Zhou Xing, Eddy Baik, Yan Jiao, Nilesh Kulkarni, Chris Li, Gautam Muralidhar, Marzieh Parandehgheibi, Erik Reed, Abhishek Singhal, Fei Xiao, Chris Pouliot
These latent embeddings can be used either as features to feed to subsequent models, such as collaborative filtering, or to build similarity metrics between songs, or to classify music based on the labels for training such as genre, mood, sentiment, etc.