Abstract: An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Abstract: An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Abstract: An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Abstract: An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Abstract: This disclosure is directed to an activity classifier system, for classifying human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. It also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. There is also an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Abstract: This disclosure is directed to an activity classifier system, for classifying human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. It also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. There is also an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Abstract: A method and system for providing a computer-simulated environment for displaying a selected mannequin wearing a combination of selected garments. In one aspect, three-dimensional scenes containing mannequin and garment objects are created within a three-dimensional modeling environment, and a simulation is performed using a cloth simulator within the modeling environment to model the construction, draping, and collision of the garment with the mannequin. Rendering frames corresponding to a variety of garments, mannequins, garment dimensions, garment styles, wearing patterns, viewing angles, and other parameters, are then generated from which images can be rendered and displayed in accordance with user requests.

Abstract: A method and system for providing a computer-simulated environment for displaying a selected mannequin wearing a combination of selected garments. In one aspect, three-dimensional scenes containing mannequin and garment objects are created within a three-dimensional modeling environment, and a simulation is performed using a cloth simulator within the modeling environment to model the construction, draping, and collision of the garment with the mannequin. Rendering frames corresponding to a variety of garments, mannequins, garment dimensions, garment styles, wearing patterns, viewing angles, and other parameters, are then generated from which images can be rendered and displayed in accordance with user requests.

Abstract: A method and system for providing a computer-simulated environment for displaying a selected mannequin wearing a combination of selected garments. In one aspect, three-dimensional scenes containing mannequin and garment objects are created within a three-dimensional modeling environment, and a simulation is performed using a cloth simulator within the modeling environment to model the construction, draping, and collision of the garment with the mannequin. Rendering frames corresponding to a variety of garments, mannequins, garment dimensions, garment styles, wearing patterns, viewing angles, and other parameters, are then generated from which images can be rendered and displayed in accordance with user requests.

Abstract: A method and system for providing a computer-simulated environment for displaying a selected mannequin wearing a combination of selected garments. In one aspect, three-dimensional scenes containing mannequin and garment objects are created within a three-dimensional modeling environment, and a simulation is performed using a cloth simulator within the modeling environment to model the construction, draping, and collision of the garment with the mannequin. Rendering frames corresponding to a variety of garments, mannequins, garment dimensions, garment styles, wearing patterns, viewing angles, and other parameters, are then generated from which images can be rendered and displayed in accordance with user requests.