Provided are systems and methods for personalized videos featuring multiple persons. An example method includes receiving a user selection of a video having at least one frame with metadata that include a first location and a second location and receiving an image of a source face and a further image of a further source face, modifying the image of the source face to generate an image of a modified source face and modifying the further image of the further source face to generate an image of a modified further source face, inserting, in the at least one frame of the video, the image of the modified source face at the first location and the image of the modified further source face at the second location to generate a personalized video, and sending the personalized video via a communication chat.

A method and an apparatus for processing an image are provided. The method may include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequence subsets divided according to similarity measurements between image sequences, each image sequence subset including a basic image sequence and other image sequence, wherein a first similarity measurement corresponding to the basic image sequence is greater than or equal to a first similarity measurement corresponding to the other image sequence; creating an original three-dimensional model using the basic image sequence; and creating a final three-dimensional model using the other image sequence based on the original three-dimensional model.

Embodiments of the present disclosure provide a method and apparatus for processing a video frame, and relates to the field of computer vision technology. The method may include: acquiring a plurality of candidate first-order radial distortion parameters preset for a to-be-processed video frame, and acquiring a specified value of a specified radial distortion parameter; performing radial distortion correction on the to-be-processed video frame to obtain a first initial corrected video frame; selecting a first initial corrected video frame in which a local region except for a center region after distortion correction includes a largest number of straight line segments; and determining a candidate first-order radial distortion parameter corresponding to the selected first initial corrected video frame for use as a target first-order radial distortion parameter of the to-be-processed video frame.

An information processing apparatus (2000) detects an abnormal region (30) from a predetermined range (16) of a video frame (14). The information processing apparatus (2000) determines whether a predetermined condition is satisfied in a case where the abnormal region (30) is detected from the predetermined range (16) of a certain video frame (14) and the abnormal region (30) is not detected from the predetermined range (16) of a predetermined video frame (14) generated later than the video frame (14). In a case where the predetermined condition is not satisfied, the information processing apparatus (2000) performs a predetermined notification.

A gardening apparatus includes one or more of a base, a fluid reservoir, and a plant tray or support disposed on the reservoir. The support is adapted for receiving one or more modular plant inserts, and can define a flow structure for channeling fluid to each insert. A pump supplies fluid from the reservoir to the plant tray or support, with a light assembly adapted to generate a spectrum of light for growth of plants from the inserts. A processor is configured for controlling fluid flow from the pump, the light spectrum generated by the lighting elements, or both. For example, the processor can use a dynamic recipe, algorithm or control schedule to modulate the fluid flow or spectrum based the plant type, growth stage, height, plant health data, digital phenotyping data, or ambient conditions, or a combination thereof.

Methods, systems, and apparatus, including medium-encoded computer program products, for pre-processing image data before 3D object tracking includes, in at least one aspect, a method including: receiving, at a first computer, image frames from a camera; identifying, by the first computer, locations of interest in the image frames; finding sequences of the locations, wherein each of the sequences satisfies a motion criterion for locations identified in at least three image frames from the camera; and sending output data for the sequences of the locations to a second computer for processing the sequences in the output data by interpolating between specified 2D positions in specific image frames for the sequences, using timestamps of the specific image frames, to produce a virtual 2D position at a predetermined point in time, which is usable for constructing a 3D track of a ball in motion.

In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

A system and method for extracting features from a 2D image of an object using a deep learning neural network and a vector field estimation process. The method includes extracting a plurality of possible feature points, generating a mask image that defines pixels in the 2D image where the object is located, and generating a vector field image for each extracted feature point that includes an arrow directed towards the extracted feature point. The method also includes generating a vector intersection image by identifying an intersection point where the arrows for every combination of two pixels in the 2D image intersect. The method assigns a score for each intersection point depending on the distance from each pixel for each combination of two pixels and the intersection point, and generates a point voting image that identifies a feature location from a number of clustered points.

A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.