In some examples, a computing device can determine synthetic meshes based on source meshes of a source mesh sequence and target meshes of a target mesh sequence. The computing device can then place the respective synthetic meshes based at least in part on a rigid transformation to define a processor-generated character. For example, the computing device can determine subsets of the mesh sequences based on a similarity criterion. The computing device can determine modified first and second meshes having a connectivity corresponding to a reference mesh. The computing device can then determine the synthetic meshes based on the modified first and second meshes. In some examples, the computing device can project source and target textures onto the synthetic mesh to provide projected source and target textures. The computing device can determine a synthetic texture registered to the synthetic mesh based on the projected source and target textures.

A method includes accessing RGB and depth image data representing a scene that includes at least a portion of a robotic limb. Using this data, a computing system may segment the image data to isolate and identify at least a portion of the robotic limb within the scene. The computing system can determine a current pose of the robotic limb within the scene based on the image data, joint data, or a 3D virtual model of the robotic limb. The computing system may then determine a desired goal pose, which may be based on the image data or the 3D virtual model. Based on the determined goal pose, the computing device determines the difference between the current pose and the goal pose of the robotic limb, and using this difference, provides a pose adjustment that for the robotic limb.

A method of automatically orienting symmetric and asymmetric food items, such as apples for example, is provided. Individual items of food are manipulated by a programmable manipulator within the view of one or more depth imaging cameras. Digital three dimensional characterizations of the surface of the food items are generated by the depth imaging camera or cameras and are utilized by a computer connected to the depth imaging camera or cameras to locate the stem and blossom of each food item. Asymmetric food items, such as apples with dropped shoulders as well as symmetric food items can be properly oriented and processed automatically.

Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

An estimation step according to an embodiment causes a computer to execute: a calculation step of using a plurality of images obtained by a plurality of imaging devices imaging a three-dimensional space in which a plurality of objects reside, to calculate representative points of pixel regions representing the objects among pixel regions of the images; a position estimation step of estimating positions of the objects in the three-dimensional space, based on the representative points calculated by the calculation step; an extraction step of extracting predetermined feature amounts from image regions representing the objects; and an attitude estimation step of estimating attitudes of the objects in the three-dimensional space, through a preliminarily learned regression model, using the positions estimated by the position estimation step, and the feature amounts extracted by the extraction step.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.

A controller of the robot apparatus performs approaching control for making a second workpiece approach a first workpiece and position adjustment control for adjusting a position of the second workpiece with respect to a position of the first workpiece. The approaching control includes control for calculating a movement direction and a movement amount of a position of the robot based on an image captured by a first camera, and making the second workpiece approach the first workpiece. The position adjustment control includes control for calculating a movement direction and a movement amount of a position of the robot based on an image captured by the first camera and an image captured by the second camera, and precisely adjusting a position of the first workpiece with respect to the second workpiece.

An example method may include receiving, at a computing device, a digital image associated with a particular media content program, the digital image containing one or more faces of particular people associated with the particular media content program. A computer-implemented face recognition program together with a set of computational models associated with the particular media content program may be applied to the digital image to recognize one or more of the particular people in the digital image, together with respective geometric coordinates for each of the one or more detected faces. At least a subset of the set of the computational models may be associated with a respective one of the particular people. The digital image together may be stored in non-transitory computer-readable memory, together with information assigning respective identities of the recognized particular people, and associating with each respective assigned identity geometric coordinates in the digital image.

There is provided mechanisms for calibration of an eye tracking system. An eye tracking system comprises a pupil centre corneal reflection (PCCR) based eye tracker and a non-PCCR based eye tracker. A method comprises obtaining at least one first eye position of a subject by applying the PCCR based eye tracker on an image set depicting the subject. The method comprises calibrating a head model of the non-PCCR based eye tracker, as applied on the image set, for the subject using the obtained at least one first eye position from the PCCR based eye tracker as ground truth. The head model comprises facial features that include at least one second eye position. The calibrating involves positioning the head model in order for its at least one second eye position to be consistent with the at least one first eye position given by the PCCR based eye tracker.

A system and method for estimating a pose of an imaging device for one or more images is provided.