Microsoft Technology Licensing, LLC

Peiran Ren

Baining Guo

Xin Tong

Jiaping Wang

Stephen S Lin

Minmin Gong

Gang Hua

John Michael Snyder

Jiaolong Yang

In a video frame processing system, a feature extractor generates, based on a plurality of data sets corresponding to a plurality of frames of a video, a plurality of feature sets, respective ones of the feature sets including features extracted from respective ones of the data sets. A first stage of the feature aggregator generates a kernel for a second stage of the feature aggregator. The kernel is adapted to content of the feature sets so as to emphasize desirable ones of the feature sets and deemphasize undesirable ones of the feature sets. In the second stage of the feature aggregator the kernel generated by the first stage is applied to the plurality of feature sets to generate a plurality of significances corresponding to the plurality of feature sets. The feature sets are weighted based on corresponding significances and weighted feature sets are aggregated to generate an aggregated feature set.

Frame aggregation network for scalable video face recognition

Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained using particular attributes from the attributes. For example, a first machine learning algorithm may be trained using a first portion of the attributes and a second machine learning algorithm may be trained using a second portion of the attributes. The trained machine learning algorithms may be used by a second computing device to render components (e.g., diffuse and specular components) of indirect shading in real-time.

Rendering global light transport in real-time using machine learning

A system for reflectance acquisition of a target includes a light source, an image capture device, and a reflectance reference chart. The reflectance reference chart is fixed relative to the target. The light source provides a uniform band of light across at least a dimension of the target. The image capture device is configured and positioned to encompass at least a portion of the target and at least a portion of the reflectance reference chart within a field-of-view of the image capture device. The image capture device captures a sequence of images of the target and the reflectance reference chart during a scan thereof. Reflectance responses are calculated for the pixels in the sequence of images. Reference reflectance response distribution functions are matched to the calculated reflectance responses, and an image of the target is reconstructed based at least in part on the matched reference reflectance response distribution functions.

Obtaining spatially varying bidirectional reflectance distribution function

Growing community of inventors

Beijing, China

Peiran Ren