Abstract: A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.

Abstract: A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.

Abstract: A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.

Abstract: The techniques disclosed herein provide systems that can control the invocation of precision input mode. A system can initially utilize a first input device, such as a head-mounted display device monitoring the eye gaze direction of a user to control the location of an input target. When one or more predetermined input gestures are detected, the system can then invoke a precision mode that transitions the control of the input target from the first input device to a second input device. The second device can include another input device utilizing different input modalities, such as a sensor detecting one or more hand gestures of the user. The predetermined input gestures can include a fixation input gesture, voice commands, or other gestures that may include the use of a user's hands or head. By controlling the invocation of precision input mode using specific gestures, a system can mitigate device coordination issues.

Abstract: A computer-implemented method for determining focus of a user is provided. User input is received. An intention image of a scene including a plurality of interactive objects is generated. The intention image includes pixels encoded with intention values determined based on the user input. An intention value indicates a likelihood that the user intends to focus on the pixel. An intention score is determined for each interactive object based on the intention values of pixels that correspond to the interactive object. An interactive object of the plurality of interactive objects is determined to be a focused object that has the user's focus based on the intention scores of the plurality of interactive objects.

Abstract: A computer-implemented method for determining focus of a user is provided. User input is received. An intention image of a scene including a plurality of interactive objects is generated. The intention image includes pixels encoded with intention values determined based on the user input. An intention value indicates a likelihood that the user intends to focus on the pixel. An intention score is determined for each interactive object based on the intention values of pixels that correspond to the interactive object. An interactive object of the plurality of interactive objects is determined to be a focused object that has the user's focus based on the intention scores of the plurality of interactive objects.