Search Results for author: Shubham Tulsiani

Found 32 papers, 15 papers with code

PixelTransformer: Sample Conditioned Signal Generation

no code implementations29 Mar 2021 Shubham Tulsiani, Abhinav Gupta

We propose a generative model that can infer a distribution for the underlying spatial signal conditioned on sparse samples e. g. plausible images given a few observed pixels.

Shelf-Supervised Mesh Prediction in the Wild

1 code implementation CVPR 2021 Yufei Ye, Shubham Tulsiani, Abhinav Gupta

We first infer a volumetric representation in a canonical frame, along with the camera pose.

Where2Act: From Pixels to Actions for Articulated 3D Objects

1 code implementation7 Jan 2021 Kaichun Mo, Leonidas Guibas, Mustafa Mukadam, Abhinav Gupta, Shubham Tulsiani

One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment.

Visual Imitation Made Easy

no code implementations11 Aug 2020 Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto

We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector.

Imitation Learning Structure from Motion

Object-Centric Multi-View Aggregation

no code implementations20 Jul 2020 Shubham Tulsiani, Or Litany, Charles R. Qi, He Wang, Leonidas J. Guibas

We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid.

Novel View Synthesis Pose Estimation

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.

Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects

1 code implementation CVPR 2020 Kiana Ehsani, Shubham Tulsiani, Saurabh Gupta, Ali Farhadi, Abhinav Gupta

Our quantitative and qualitative results show that (a) we can predict meaningful forces from videos whose effects lead to accurate imitation of the motions observed, (b) by jointly optimizing for contact point and force prediction, we can improve the performance on both tasks in comparison to independent training, and (c) we can learn a representation from this model that generalizes to novel objects using few shot examples.

Human-Object Interaction Detection

Intrinsic Motivation for Encouraging Synergistic Behavior

no code implementations ICLR 2020 Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, Abhinav Gupta

Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.

Discovering Motor Programs by Recomposing Demonstrations

no code implementations ICLR 2020 Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta

In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.

Hierarchical Reinforcement Learning

Object-centric Forward Modeling for Model Predictive Control

1 code implementation8 Oct 2019 Yufei Ye, Dhiraj Gandhi, Abhinav Gupta, Shubham Tulsiani

We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals.

Efficient Bimanual Manipulation Using Learned Task Schemas

no code implementations30 Sep 2019 Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, Abhinav Gupta

Our insight is that for many tasks, the learning process can be decomposed into learning a state-independent task schema (a sequence of skills to execute) and a policy to choose the parameterizations of the skills in a state-dependent manner.

Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency

1 code implementation7 May 2019 Tejas Khot, Shubham Agrawal, Shubham Tulsiani, Christoph Mertz, Simon Lucey, Martial Hebert

We demonstrate our ability to learn MVS without 3D supervision using a real dataset, and show that each component of our proposed robust loss results in a significant improvement.

Depth Estimation

Layer-structured 3D Scene Inference via View Synthesis

1 code implementation ECCV 2018 Shubham Tulsiani, Richard Tucker, Noah Snavely

We present an approach to infer a layer-structured 3D representation of a scene from a single input image.

Learning Category-Specific Mesh Reconstruction from Image Collections

no code implementations ECCV 2018 Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, Jitendra Malik

The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation.

Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction

no code implementations CVPR 2018 Shubham Tulsiani, Alexei A. Efros, Jitendra Malik

We present a framework for learning single-view shape and pose prediction without using direct supervision for either.

Pose Prediction

Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene

no code implementations CVPR 2018 Shubham Tulsiani, Saurabh Gupta, David Fouhey, Alexei A. Efros, Jitendra Malik

The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in terms of a small set of factors: a layout representing the enclosing surfaces as well as a set of objects represented in terms of shape and pose.

Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency

no code implementations CVPR 2017 Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, Jitendra Malik

We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view.

Hierarchical Surface Prediction for 3D Object Reconstruction

1 code implementation3 Apr 2017 Christian Häne, Shubham Tulsiani, Jitendra Malik

A major limitation of such approaches is that they only predict a coarse resolution voxel grid, which does not capture the surface of the objects well.

3D Object Reconstruction

Learning Shape Abstractions by Assembling Volumetric Primitives

1 code implementation CVPR 2017 Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, Jitendra Malik

We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives.

View Synthesis by Appearance Flow

4 code implementations11 May 2016 Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, Alexei A. Efros

We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints.

Novel View Synthesis

Shape and Symmetry Induction for 3D Objects

no code implementations24 Nov 2015 Shubham Tulsiani, Abhishek Kar, Qi-Xing Huang, João Carreira, Jitendra Malik

Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint.

General Classification Object Classification

Amodal Completion and Size Constancy in Natural Scenes

no code implementations ICCV 2015 Abhishek Kar, Shubham Tulsiani, João Carreira, Jitendra Malik

We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image.

Object Detection Object Recognition +1

Pose Induction for Novel Object Categories

1 code implementation ICCV 2015 Shubham Tulsiani, João Carreira, Jitendra Malik

We address the task of predicting pose for objects of unannotated object categories from a small seed set of annotated object classes.

Category-Specific Object Reconstruction from a Single Image

no code implementations CVPR 2015 Abhishek Kar, Shubham Tulsiani, João Carreira, Jitendra Malik

Object reconstruction from a single image -- in the wild -- is a problem where we can make progress and get meaningful results today.

Object Detection Object Reconstruction

Viewpoints and Keypoints

no code implementations CVPR 2015 Shubham Tulsiani, Jitendra Malik

We characterize the problem of pose estimation for rigid objects in terms of determining viewpoint to explain coarse pose and keypoint prediction to capture the finer details.

Keypoint Detection

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