Search Results for author:
Found 15 papers, 1 papers with code
On the challenges to learn from Natural Data Streams
In real-world contexts, sometimes data are available in form of Natural Data Streams, i. e. data characterized by a streaming nature, unbalanced distribution, data drift over a long time frame and strong correlation of samples in short time ranges.
SHREC 2022 Track on Online Detection of Heterogeneous Gestures
This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses.
Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps
Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes.
SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more.
Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face?
Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
Recently, deep learning approaches have achieved promising results in various fields of computer vision.
Learn to See by Events: Color Frame Synthesis from Event and RGB Cameras
Event cameras are biologically-inspired sensors that gather the temporal evolution of the scene.
Learning to Generate Facial Depth Maps
In this paper, an adversarial architecture for facial depth map estimation from monocular intensity images is presented.
Face-from-Depth for Head Pose Estimation on Depth Images
Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only.
Head Detection with Depth Images in the Wild
Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images.
Learning to Map Vehicles into Bird's Eye View
Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies.
From Depth Data to Head Pose Estimation: a Siamese approach
In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner.
Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons
HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism.
Deep Head Pose Estimation from Depth Data for In-car Automotive Applications
Recently, deep learning approaches have achieved promising results in various fields of computer vision.
POSEidon: Face-from-Depth for Driver Pose Estimation
In this work, we present a new deep learning framework for head localization and pose estimation on depth images.
