107 citations · 213 across the 18 of their papers we have counts for
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Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
Jogendra Nath Kundu, Maharshi Gor, Phani Krishna Uppala +1
An unsupervised human action modeling framework can provide useful pose-sequence representation, which can be utilized in a variety of pose analysis applications. In this work we p…
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu
Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose dynamic…
Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence
Jogendra Nath Kundu, Rahul M. V., Aditya Ganeshan +1
Understanding the geometry and pose of objects in 2D images is a fundamental necessity for a wide range of real world applications. Driven by deep neural networks, recent methods h…
iSPA-Net: Iterative Semantic Pose Alignment Network
Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V. +2
Understanding and extracting 3D information of objects from monocular 2D images is a fundamental problem in computer vision. In the task of 3D object pose estimation, recent data d…
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation
Jogendra Nath Kundu, Phani Krishna Uppala, Anuj Pahuja +1
Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccu…