We present an end-to-end trainable Neural Network architecture for stereo imaging that jointly locates and estimates human body poses in 3D. Our method defines a 2D pose for each human in a stereo pair of images and uses a correlation layer with a composit ...
This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying hei ...
Reconstructing objects in three dimensions (3D) has always been one of the primary tasks in the domain of computer graphics (CG). Succeeding in this task in real-time, including texture reconstruction, and with very low-cost hardware is the challenge prese ...
Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of ...
Institute of Electrical and Electronics Engineers2008
In this work, we propose new ways to learn pose and motion priors models and show that they can be used to increase the performance of 3D body tracking algorithms, resulting in very realistic motions under very challenging conditions. We first explored an ...
