What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Yaser Sheikh mostly deals with Artificial intelligence, Computer vision, Motion capture, Pose and Image.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Linear combination.
His Machine learning course of study focuses on Inference and Structured prediction, Feature, 3D pose estimation and Graphical model.
Computer vision is closely attributed to Representation in his research.
His research in Motion capture intersects with topics in Tomographic reconstruction, Voxel, Rendering and Structure from motion.
His Pose study integrates concerns from other disciplines, such as Key and Pattern recognition.
His most cited work include:
- Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields (2565 citations)
- Convolutional Pose Machines (1304 citations)
- OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields (624 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Motion, Image and Face.
The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways.
Computer vision connects with themes related to Computer graphics in his study.
Yaser Sheikh has researched Motion in several fields, including Studio and Host.
His studies deal with areas such as Autoencoder, Facial expression, Reduction and Virtual reality as well as Face.
His Pose study combines topics in areas such as Machine learning and Inference.
He most often published in these fields:
- Artificial intelligence (80.72%)
- Computer vision (68.07%)
- Motion (12.05%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.72%)
- Computer vision (68.07%)
- Human–computer interaction (6.63%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Human–computer interaction, Codec and Virtual reality.
His study in the fields of Facial recognition system, Object detection and Gesture under the domain of Artificial intelligence overlaps with other disciplines such as Scalability and Expression.
His work on Key expands to the thematically related Computer vision.
His Virtual reality research integrates issues from Computer facial animation, Face and Gaze.
His work in Triangulation addresses issues such as Motion capture, which are connected to fields such as Divide and conquer algorithms and Human-body model.
His research investigates the connection between Augmented reality and topics such as Optical head-mounted display that intersect with problems in Representation.
Between 2019 and 2021, his most popular works were:
- OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields (624 citations)
- 4D Visualization of Dynamic Events From Unconstrained Multi-View Videos (11 citations)
- Audio- and Gaze-Driven Facial Animation of Codec Avatars (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Computer vision, Artificial intelligence, Key, Human–computer interaction and Codec.
His Computer vision study is mostly concerned with Facial recognition system, Pose, Object detection, Kernel and Triangulation.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Measure.
His study in Key is interdisciplinary in nature, drawing from both Visualization, Viewpoints and Iterative reconstruction.
His Human–computer interaction study incorporates themes from Motion, Gaze, Computer facial animation, Face and Eye tracking.
His work deals with themes such as Representation, Facial expression, Virtual reality and Avatar, which intersect with Codec.
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