Abstract: An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of an object. Furthermore, the apparatus includes a memory storage unit to store the raw data. The apparatus also includes a pre-processing engine to generate a coarse segmentation map and a joint heatmap from the raw data. The coarse segmentation map is to outline the object and the joint heatmap is to represent a point on the object. The apparatus further includes a neural network engine to receive the raw data, the coarse segmentation map, and the joint heatmap. The neural network engine is to generate a plurality of two-dimensional maps. Also, the apparatus includes a mesh creator engine to generate a three-dimensional mesh based on the plurality of two-dimensional maps.

Abstract: An apparatus is provided to generate mechanical index maps for mesh rigging. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of an object. The apparatus further includes a memory storage unit to store the raw data. In addition, the apparatus includes a pre-processing engine to generate a segmentation map from the raw data. The segmentation map is to outline the object. Furthermore, the apparatus includes a neural network engine to generate a mechanical heatmap for a predefined key-point connector based on the segmentation map. The mechanical heatmap includes a mechanical weight index of the predefined key-point connector for each pixel.

Abstract: Introduced here is an approach to allowing a computer-implemented model to learn its own ordering scheme by applying an appropriate loss function during training. More generally, the present disclosure pertains to computer programs and associated computer-implemented techniques for estimating pose of a living body through simultaneous analysis of multiple visualizations. For example, joint type heatmaps—where a given heatmap includes every visible joint of the corresponding type across all visible persons—can be combined with joint person heatmaps—where a given heatmap includes every visible joint of the corresponding person—to better understand the relationship between joints visible in a digital image and people included in the digital image.

Abstract: Introduced here is an approach to allowing a computer-implemented model to learn its own ordering scheme by applying an appropriate loss function during training. More generally, the present disclosure pertains to computer programs and associated computer-implemented techniques for estimating pose of a living body through simultaneous analysis of multiple visualizations. For example, joint type heatmaps—where a given heatmap includes every visible joint of the corresponding type across all visible persons—can be combined with joint person heatmaps—where a given heatmap includes every visible joint of the corresponding person—to better understand the relationship between joints visible in a digital image and people included in the digital image.

Abstract: An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of an object. Furthermore, the apparatus includes a memory storage unit to store the raw data. The apparatus also includes a pre-processing engine to generate a coarse segmentation map and a joint heatmap from the raw data. The coarse segmentation map is to outline the object and the joint heatmap is to represent a point on the object. The apparatus further includes a neural network engine to receive the raw data, the coarse segmentation map, and the joint heatmap. The neural network engine is to generate a plurality of two-dimensional maps. Also, the apparatus includes a mesh creator engine to generate a three-dimensional mesh based on the plurality of two-dimensional maps.

Abstract: Introduced here are computer-implemented platforms (also referred to as “pose monitoring platforms”) that are designed to improve adherence to, and success of, programs requiring performance of physical activities. As part of a program, a participant may be requested to engage with a pose monitoring platform to perform a single physical activity, multiple repetitions of a single physical activity, or multiple repetitions of multiple physical activities. The pose monitoring platform can determine, for example, using a neural network that has parallel branches, whether digital images of the participant's environment includes certain body parts and then estimate poses of those body parts. The pose monitoring platform can use the estimated poses to guide the participant through the program, such as by providing instructions for performing the physical activities.

Abstract: Introduced here are computer-implemented platforms (also referred to as “pose monitoring platforms”) that are designed to improve adherence to, and success of, programs requiring performance of physical activities. As part of a program, a participant may be requested to engage with a pose monitoring platform to perform a single physical activity, multiple repetitions of a single physical activity, or multiple repetitions of multiple physical activities. The pose monitoring platform can determine, for example, using a neural network that has parallel branches, whether digital images of the participant's environment includes certain body parts and then estimate poses of those body parts. The pose monitoring platform can use the estimated poses to guide the participant through the program, such as by providing instructions for performing the physical activities.

Abstract: An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of a first object and a second object. The apparatus further includes a memory storage unit to store the raw data. In addition, the apparatus includes a neural network engine to receive the raw data. The neural network engine is to generate a segmentation map and a boundary map. The apparatus also includes a post-processing engine to identify the first object and the second object based on the segmentation map and the boundary map.

Abstract: An apparatus is provided. The apparatus includes a communications interface to receive raw data. The raw data includes a representation of an actual object in two-dimension. The apparatus further includes a memory storage unit to store the raw data and reference data. In addition, the apparatus includes a scale estimation engine to receive the raw data and the reference data. The scale estimation engine is to calculate a first root position of the actual object in a three-dimensional space based on an analysis of the raw data with the reference data. Furthermore, the apparatus includes an aggregator to generate output data based on the first root position. The output data is to be transmitted to an external device.

Abstract: An apparatus is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a representation of an object. Furthermore, the apparatus includes a memory storage unit to store the raw data. The apparatus also includes a pre-processing engine to generate a coarse segmentation map and a joint heatmap from the raw data. The coarse segmentation map is to outline the object and the joint heatmap is to represent a point on the object. The apparatus further includes a neural network engine to receive the raw data, the coarse segmentation map, and the joint heatmap. The neural network engine is to generate a plurality of two-dimensional maps. Also, the apparatus includes a mesh creator engine to generate a three-dimensional mesh based on the plurality of two-dimensional maps.

Abstract: An example of an apparatus to infer joint rotations and positions is provided. The apparatus includes a communications interface to receive raw data from an external source. The raw data includes a first joint position and a second joint position of an input skeleton. In addition, the apparatus includes a memory storage unit to store the raw data. Furthermore, the apparatus includes a pre-processing engine to generate normalized data from the raw data. The normalized data is to be stored in the memory storage unit. Also, the apparatus includes an inverse kinematics engine to apply a neural network to infer a joint rotation from the normalized data. The neural network is to use historical data. Training data used to train the neural network includes positional noise.

Abstract: An example of an apparatus for markerless motion capture is provided. The apparatus includes cameras to capture images of a subject from different perspectives. In addition, the apparatus includes a pose estimation engines to receive the images. Each pose estimation engine is to generate a coarse skeletons of the received image and is to identify a region of the image based on the coarse skeleton. Furthermore, the apparatus includes pose estimation engines to receive the regions of interest previously identified. Each of these pose estimation engines is to generate a fine skeleton of the region of interest. In addition, the apparatus includes attachment engines to generate a whole skeletons. Each whole skeleton is to include a fine skeleton attached to a coarse skeleton. The apparatus further includes an aggregator to receive the whole skeletons. The aggregator is to generate a three-dimensional skeleton from the whole skeletons.