Amazon Technologies, Inc.

Walterio Wolfgang Mayol Cuevas

Nataliya Shapovalova

Silviu Stefan Andrei

Wasiq Bokhari

William J Vass

Sean Love

Garish Sood

Tejas Shamrao Khot

Lorien S Miller

Monica Jean Parsons

Glenn Andrew Dyck

Sarah Maurine Role Schaffer

William Vass

Systems and methods for computer-guided item creation and interaction include causing display of item creation templates and directions for creating an item from an object. Image data and infrared data is collected and utilized during item creation to determine user movement deviations and object shape deviations from the directions to provide real time corrective guidance and to determine how to augment the templates and/or directions. Once created, the item is analyzed to determine differences between it and a reference item such that displayed content is augmented when the created item is interacting with a surface.

Computer-guided item creation and interaction

Techniques for enhancing an image are described. For example, a lower-resolution image from a video file may be enhanced using a trained neural network applying the trained neural network on the lower-resolution image to remove artifacts by removing artifacts by generating, using a layer of the trained neural network, a residual value based on the proper subset of the received image and at least one corresponding image portion of a preceding lower resolution image in the video file and at least one corresponding image portion of a subsequent lower resolution image in the video file, upscale the lower-resolution image using bilinear upsampling, and combine the upscaled received image and residual value to generate an enhanced image.

Video enhancement using a neural network

Techniques for video enhancement using a service are described. In some examples, a video enhancement service is to receive, from a requesting device, a request to provide a video, the request including an indication that at least a proper subset of the video is to be enhanced using at least one machine learning model; select at least one machine learning model to be used to enhance at least the proper subset of the video; enhance at least the proper subset of the video using the selected at least one machine learning model; and output the video including the at least proper subset of the video that had been enhanced.

Video enhancement service

Systems and methods to encode sounds in association with containers, as well as systems and methods to recognize encoded sounds associated with containers, are described. The containers may include various structures to encode sounds. Responsive to opening of containers, the various structures may emit the encoded sounds. An audio capture device may receive the encoded sounds, and a computing device may process the encoded sounds to identify audio signatures within the encoded sounds. The audio signatures may be matched with known audio signatures to identify the containers and/or information associated with the containers.

Systems and methods to encode sounds in association with containers

An input video stream corresponding to input video content may be received over one or more networks. The input video stream may include a first image frame. An input visual feature of the first image frame may be matched to a reference visual feature of a reference image. The reference image may have a higher image quality than the first image frame. A replacement visual feature may be generated for the input visual feature. The replacement visual feature may be generated based at least in part on the reference visual feature. An enhanced image frame corresponding to the first image frame may be generated by at least replacing the input visual feature in the first image frame with the replacement visual feature. An enhanced video stream may be provided. The enhanced image frame may be a substitute for the first image frame in the enhanced video stream.

Reference-based streaming video enhancement

Techniques for enhancing an image are described. For example, a lower-resolution image from a video file may be enhanced using a trained neural network applying the trained neural network on the lower-resolution image to remove artifacts by generating, using a layer of the trained neural network, a residual value based on the proper subset of the received image and at least one corresponding image portion of a previously generated higher resolution image in the video file, upscaling the received image using bilinear upsampling, and combining the upscaled received image and residual value to generate an enhanced image.

Video enhancement using a recurrent image date of a neural network

Techniques for enhancing an image are described. For example, a lower-resolution image, for example from a video file, may be enhanced using a trained neural network by applying the trained neural network to enhance a middle lower-resolution image of a plurality of lower-resolution images using a generator with filters of a generative adversary network. In some examples, a plurality of sequential feature processing acts are performed on the lower-resolution images to generate a residual which is added to a filtered version of one of the lower-resolution images to generate an enhanced image.

Video enhancement using a generator with filters of generative adversarial network

Systems and methods to initiate actions based on encoded sounds associated with containers are described. Responsive to opening of containers, various structures may emit the encoded sounds. The encoded sounds may be received, processed, and recognized to identify audio signatures within the encoded sounds. Containers and information associated therewith may be identified based on the identified audio signatures. One or more actions or outputs may be initiated related to the containers or information associated therewith using various output devices.

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Seattle, WA, United States of America

Walterio Wolfgang Mayol Cuevas