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Found 4 papers, 3 papers with code
Learning from synthetic data generated with GRADE
To solve this, we present a fully customizable framework for generating realistic animated dynamic environments (GRADE) for robotics research, first introduced in [1].
Synthetic Data-based Detection of Zebras in Drone Imagery
Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training.
AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation
In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation.
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning
We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape of a single moving person using multiple micro aerial vehicles.
