Techniques for inferring three-dimensional poses from two-dimensional images

• Main Authors: Borer, Dominik Tobias, Nitti, Maurizio, Guay, Martin, Buhmann, Jakob Joachim
• Format: Patent
• Language: English
• Published: 06.06.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

In various embodiments, a training application generates training items for three-dimensional (3D) pose estimation. The training application generates multiple posed 3D models based on multiple 3D poses and a 3D model of a person wearing a costume that is associated with multiple visual attributes. For each posed 3D model, the training application performs rendering operation(s) to generate synthetic image(s). For each synthetic image, the training application generates a training item based on the synthetic image and the 3D pose associated with the posed 3D model from which the synthetic image was rendered. The synthetic images are included in a synthetic training dataset that is tailored for training a machine-learning model to compute estimated 3D poses of persons from two-dimensional (2D) input images. Advantageously, the synthetic training dataset can be used to train the machine-learning model to accurately infer the orientations of persons across a wide range of environments.