The present disclosure provides a dimensional measurement method and device based on deep learning. The method includes capturing a target image of a target object, obtaining measurement data for the target image, and determining whether or not the target object is within a preset tolerance.

Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.

Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.

Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter μ.sub.y, an entropy scale parameter σy, and a context matrix A.sub.y of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μ.sub.y and the context matrix A.sub.y, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.

Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter μ.sub.y, an entropy scale parameter σy, and a context matrix A.sub.y of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μ.sub.y and the context matrix A.sub.y, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.

A method and an apparatus for labeling human body completeness data, and a terminal device, are provided. The method includes: obtaining an image to be labeled (201); performing human body detection on the image to obtain a first human body frame (202); performing human body key point detection on the image, and determining human body part information according to the human body key points that have been detected (203); performing human body area detection on the image to obtain human body visible region labeling information (204); determining the human body part information associated with the first human body frame, and determining the human body visible region labeling information associated with the first human body frame, to complete the labeling of human body completeness data of the first human body frame (205). The described method can reduce a lot of manpower and material resources, shorten the time for labeling human completeness data, and are benefit rapid iteration of products.

A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

An aspect of the disclosure is to determine a watch face image, and an operation method of an electronic device according to various embodiments may include: acquiring an image using a camera of the electronic device; displaying a watch face preview matching the acquired image on a display of the electronic device; determining a watch face image from the watch face preview; controlling a transceiver of the electronic device to transmit the watch face image to a smart watch; and applying the watch face image to the smart watch.

An aspect of the disclosure is to determine a watch face image, and an operation method of an electronic device according to various embodiments may include: acquiring an image using a camera of the electronic device; displaying a watch face preview matching the acquired image on a display of the electronic device; determining a watch face image from the watch face preview; controlling a transceiver of the electronic device to transmit the watch face image to a smart watch; and applying the watch face image to the smart watch.

A method and system for virtual environment-based interfaces applied to selected objects from video directs a system's focus of attention to an image within a first video stream and identifies an object in the image by applying a trained neural network. In response to a communication from a user comprising language and/or images describing a virtual environment, a second trained neural network is applied to generate a second video stream that embodies the identified object within a virtual environment that is in accordance with the user-described virtual environment. The second video stream is then delivered to the user. The system's focus of attention and/or generation of the virtual environment may be informed by user preferences that are inferred from user behaviors.