Search Results for author: Nilesh Kulkarni

Found 14 papers, 4 papers with code

3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surface

no code implementations13 Mar 2024 Linyi Jin, Nilesh Kulkarni, David Fouhey

This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images.

3D Reconstruction

NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

no code implementations14 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.

Motion Synthesis valid

Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data

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.

3D Reconstruction

Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?

no code implementations3 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.

Implicit Mesh Reconstruction from Unannotated Image Collections

no code implementations16 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.

Articulation-aware Canonical Surface Mapping

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.

On-Device Neural Language Model Based Word Prediction

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.

Automatic Speech Recognition (ASR) Language Modelling +4

Syllable-level Neural Language Model for Agglutinative Language

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.

Language Modelling

An Embedded Deep Learning based Word Prediction

1 code implementation6 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.

Language Modelling Machine Translation +1

Modeling of the Latent Embedding of Music using Deep Neural Network

no code implementations12 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.

Collaborative Filtering

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