8 citations · 10 across the 5 of their papers we have counts for
6 papers
PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation
Soubarna Banik, Edvard Avagyan, Sayantan Auddy +2
Existing skeleton-based 3D human pose estimation methods only predict joint positions. Although the yaw and pitch of bone rotations can be derived from joint positions, the roll ar…
Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation
Soubarna Banik, Patricia Gschoßmann, Alejandro Mendoza Garcia +1
Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In spite of the large amount of research dedicated to 3D HPE, only a limited number of studies address the…
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control
Marco Oliva, Soubarna Banik, Josip Josifovski +1
State-of-the-art reinforcement learning algorithms predominantly learn a policy from either a numerical state vector or images. Both approaches generally do not take structural kno…
Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation
Soubarna Banik, Alejandro Mendoza Garcia, Lorenz Kiwull +2
Posture estimation using a single depth camera has become a useful tool for analyzing movements in rehabilitation. Recent advances in posture estimation in computer vision research…
3D Human Pose Regression using Graph Convolutional Network
Soubarna Banik, Alejandro Mendoza Gracia, Alois Knoll
3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of t…
Multi-label Object Attribute Classification using a Convolutional Neural Network
Soubarna Banik, Mikko Lauri, Simone Frintrop
Objects of different classes can be described using a limited number of attributes such as color, shape, pattern, and texture. Learning to detect object attributes instead of only…