Search Results for author: Srinath Sridhar

Found 21 papers, 8 papers with code

ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes

no code implementations19 Jan 2022 Rahul Sajnani, Adrien Poulenard, Jivitesh Jain, Radhika Dua, Leonidas J. Guibas, Srinath Sridhar

ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.

Learning Body-Aware 3D Shape Generative Models

no code implementations13 Dec 2021 Bryce Blinn, Alexander Ding, Daniel Ritchie, R. Kenny Jones, Srinath Sridhar, Manolis Savva

The body-shape-conditioned models produce chairs which will be comfortable for a person with the given body shape; the pose-conditioned models produce chairs which accommodate the given sitting pose.

Neural Fields in Visual Computing and Beyond

no code implementations22 Nov 2021 Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, Srinath Sridhar

Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time.

3D Reconstruction Image Animation +1

StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects

no code implementations17 May 2021 Ge Zhang, Or Litany, Srinath Sridhar, Leonidas Guibas

We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.

3D Reconstruction

Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images

1 code implementation ECCV 2020 Jiahui Lei, Srinath Sridhar, Paul Guerrero, Minhyuk Sung, Niloy Mitra, Leonidas J. Guibas

We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

1 code implementation NeurIPS 2020 Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas

We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects.

Pose Estimation

Representation Learning Through Latent Canonicalizations

no code implementations26 Feb 2020 Or Litany, Ari Morcos, Srinath Sridhar, Leonidas Guibas, Judy Hoffman

We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision.

Representation Learning

Continuous Geodesic Convolutions for Learning on 3D Shapes

no code implementations6 Feb 2020 Zhangsihao Yang, Or Litany, Tolga Birdal, Srinath Sridhar, Leonidas Guibas

In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.

Predicting the Physical Dynamics of Unseen 3D Objects

1 code implementation16 Jan 2020 Davis Rempe, Srinath Sridhar, He Wang, Leonidas J. Guibas

Experiments show that we can accurately predict the changes in state for unseen object geometries and initial conditions.

Multiview Aggregation for Learning Category-Specific Shape Reconstruction

1 code implementation NeurIPS 2019 Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas

We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.

3D Shape Reconstruction

Learning Generalizable Physical Dynamics of 3D Rigid Objects

no code implementations2 Jan 2019 Davis Rempe, Srinath Sridhar, He Wang, Leonidas J. Guibas

In this work, we focus on predicting the dynamics of 3D rigid objects, in particular an object's final resting position and total rotation when subjected to an impulsive force.

Autonomous Vehicles

Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

6 code implementations9 Dec 2017 Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Srinath Sridhar, Gerard Pons-Moll, Christian Theobalt

Our approach uses novel occlusion-robust pose-maps (ORPM) which enable full body pose inference even under strong partial occlusions by other people and objects in the scene.

3D Pose Estimation

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

1 code implementation3 May 2017 Dushyant Mehta, Srinath Sridhar, Oleksandr Sotnychenko, Helge Rhodin, Mohammad Shafiei, Hans-Peter Seidel, Weipeng Xu, Dan Casas, Christian Theobalt

A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton.

3D Human Pose Estimation

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

no code implementations16 Oct 2016 Srinath Sridhar, Franziska Mueller, Michael Zollhöfer, Dan Casas, Antti Oulasvirta, Christian Theobalt

However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.

Object Tracking

Fast and Robust Hand Tracking Using Detection-Guided Optimization

no code implementations CVPR 2015 Srinath Sridhar, Franziska Mueller, Antti Oulasvirta, Christian Theobalt

In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth.

Pose Estimation

Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model

no code implementations11 Feb 2016 Srinath Sridhar, Helge Rhodin, Hans-Peter Seidel, Antti Oulasvirta, Christian Theobalt

In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time.

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