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Research Associate

Universidad Rey Juan Carlos

Autonomous driving in unstructured environments.

Optical Team Lead

SEDDI

Responsible for overseeing the daily operations of a small team of R&D engineers, focusing on planning, reviewing, and delivering complex computer vision projects. These projects leveraged advanced, deep learning-based solutions. Alongside my leadership duties, I maintained an active role in the development process, ensuring hands-on involvement from concept to implementation.

Computer Vision Engineer

SEDDI

As a computer vision engineer at SEDDI, I contributed to the development of its flagship product, textura.ai, from its inception. Joining the company at a very early stage allowed me to be involved in every step of the process. I contributed to designing a complex multi-camera and multi-illumination digital material acquisition setup, developed tools to extract textile properties using both classic computer vision and deep learning methods, and implemented solutions for color constancy and calibration across various imaging devices.

Immersive Video Researcher

Nokia Bell Labs

As an immersive video researcher, my internship primarily focused on the field of Human-Computer Interaction, specifically on developing user interfaces based on hand gesture recognition. Additionally, I became familiar with augmented, mixed, and virtual reality concepts and techniques.

Computer Vision Engineer

Bosch

As a machine vision engineer intern, I provided support to the Process Engineering Department by engaging in a variety of tasks. These included studying Data Matrix reading systems and optical lenses, as well as developing an optical calculator. Additionally, I diagnosed and designed computer vision systems and developed computer vision applications with Neurocheck.

Volunteer

SEO/BirdLife

Mentor

Google Summer of Code

Software Developer

JdeRobot

Doctor of Philosophy - PhD

Universidad Rey Juan Carlos

Computer vision

Master's degree

URJC

Computer Vision

Erasmus +

Óbuda University

Electrical Engineering

Bachelor's degree

Universidad Rey Juan Carlos

Audiovisual and Multimedia Systems Engineering

Efficient 3D human pose estimation from RGBD sensors

Displays

Human pose estimation is a core component in applications for which some level of human–computer interaction is required, such as assistive robotics, ambient assisted living or the motion capture systems used in biomechanics or video games production. In this paper, we propose an end-to-end pipeline for estimating 3D human poses that works in real-time in an off-the-shelf computer, using as input video sequences captured with a commercial RGBD sensor. Our hybrid approach is composed of two stages: 2D pose estimation using deep neural networks and 3D registration, for which a lightweight algorithm based on classic computer vision techniques has been developed. We compare several 2D pose estimators and validate the performance of our proposed method against the state-of-the-art, using as benchmark an international and publicly available dataset. Our 2D to 3D registration module alone can reach frame rates of up to 99 fps, while achieving an average error per joint of 132 mm. Furthermore, the proposed solution is agnostic to the model used for 2D pose estimation and can be upgraded with new upcoming solutions or adapted for different articulated objects.