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Weakly-supervised domain adaptation via gan and mesh model for estimating 3d hand poses interacting objects
Despite recent successes in hand pose estimation, there yet remain challenges on RGB-
based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where …
based 3D hand pose estimation (HPE) under hand-object interaction (HOI) scenarios where …
TriHorn-net: a model for accurate depth-based 3D hand pose estimation
Abstract 3D hand pose estimation methods have made significant progress recently.
However, estimation accuracy is often far from sufficient for specific real-world applications …
However, estimation accuracy is often far from sufficient for specific real-world applications …
Contextual attention for hand detection in the wild
We present Hand-CNN, a novel convolutional network architecture for detecting hand masks
and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN …
and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN …
Learning human-to-robot dexterous handovers for anthropomorphic hand
Human–robot interaction plays an important role in robots serving human production and
life. Object handover between humans and robotics is one of the fundamental problems of …
life. Object handover between humans and robotics is one of the fundamental problems of …
FORTE: Few samples for recognizing hand gestures with a smartphone-attached radar
Radar sensing technologies offer several advantages over other gesture input modalities,
such as the ability to reliably sense human movements, a reasonable deployment cost …
such as the ability to reliably sense human movements, a reasonable deployment cost …
Weakly-supervised hand part segmentation from depth images
Existing learning-based methods require a large number of labeled data to produce
accurate part segmentation labels. However, acquiring ground truth labels is costly, giving …
accurate part segmentation labels. However, acquiring ground truth labels is costly, giving …
Ego2hands: A dataset for egocentric two-hand segmentation and detection
Hand segmentation and detection in truly unconstrained RGB-based settings is important for
many applications. However, existing datasets are far from sufficient in terms of size and …
many applications. However, existing datasets are far from sufficient in terms of size and …
Hadr: Applying domain randomization for generating synthetic multimodal dataset for hand instance segmentation in cluttered industrial environments
This study uses domain randomization to generate a synthetic RGB-D dataset for training
multimodal instance segmentation models, aiming to achieve colour-agnostic hand …
multimodal instance segmentation models, aiming to achieve colour-agnostic hand …
Egocentric upper limb segmentation in unconstrained real-life scenarios
The segmentation of bare and clothed upper limbs in unconstrained real-life environments
has been less explored. It is a challenging task that we tackled by training a deep neural …
has been less explored. It is a challenging task that we tackled by training a deep neural …
Generating synthetic depth image dataset for industrial applications of hand localization
In this paper, we focus on the problem of applying domain randomization to produce
synthetic datasets for training depth image segmentation models for the task of hand …
synthetic datasets for training depth image segmentation models for the task of hand …