Abstract: An object classification model training method, a computation device for training an object classification model, an object recognition device, and an object recognition method are provided. The object classification model training method comprises steps of: receiving multiple model files each respectively having a virtual object 3D model; generating multiple 2D images of each virtual object 3D model by a revolving image-capture schedule, wherein the positions of the virtual object 3D model in the multiple 2D images are different; generating a training data set for each virtual object 3D model based on the multiple 2D images of each virtual object 3D model; and training an untrained model according to the training data sets corresponding to the virtual object 3D models of the multiple model files to obtain an object classification model.

Abstract: A method of correcting depth of field, performed by a first MR head-mounted device, comprises: superimposing a first virtual object on a first background image to generate an superimposed image, inputting the superimposed image into a depth-of-field correction model to generate a displayed image and display the displayed image, receiving at least one user feedback signal corresponding to the displayed image, and updating a depth-of-field parameter of the depth-of-field correction model with the at least one user feedback signal for the depth-of-field correction model to generate an updated virtual object.

Abstract: A video generating device and method are provided. The device analyzes a plurality of real-time images corresponding to a plurality of users to segment a target image from each of the real-time images. The device generates a three-dimensional portrait model corresponding to each of the users based on the target image of each of the real-time images. The device determines a first three-dimensional scenario template from the three-dimensional scenario templates based on a user quantity of the users and the position quantity corresponding to each of the three-dimensional scenario templates. The device composites the three-dimensional portrait models to the spatial label position of the first three-dimensional scenario template to generate a video corresponding to the users.

Abstract: A video generating device and method are provided. The device analyzes a plurality of real-time images corresponding to a plurality of users to segment a target image from each of the real-time images. The device generates a three-dimensional portrait model corresponding to each of the users based on the target image of each of the real-time images. The device determines a first three-dimensional scenario template from the three-dimensional scenario templates based on a user quantity of the users and the position quantity corresponding to each of the three-dimensional scenario templates. The device composites the three-dimensional portrait models to the spatial label position of the first three-dimensional scenario template to generate a video corresponding to the users.

Abstract: A method of correcting depth of field, performed by a first MR head-mounted device, comprises: superimposing a first virtual object on a first background image to generate an superimposed image, inputting the superimposed image into a depth-of-field correction model to generate a displayed image and display the displayed image, receiving at least one user feedback signal corresponding to the displayed image, and updating a depth-of-field parameter of the depth-of-field correction model with the at least one user feedback signal for the depth-of-field correction model to generate an updated virtual object.

Abstract: A computing system, method and computer readable media for group interactive multicast using UPnP AV architecture on a network are disclosed. The system comprises a plurality of devices and control points coupled to the network. The devices include a plurality of renders and a server, in which those renders form a group. The server comprises a data recording medium for saving a group list of the group. The control points comprise a server control point and a plurality of render control points. The control points are utilized to control the server and the renders and to register events. As one of the render control points correspondingly sends a command to one of the renders of the group, the render, which receives the command, will feedback a UPnP event to the server. The server will transfer the command to those renders, which do not receive the command before, so as to make those renders of the group to execute the command simultaneously.