Amazon Technologies, Inc.

Yashal Shakti Kanungo

Sumit Negi

Pooja Ashok Kumar

Sai Sree Harsha

Shilpa Pundi Ananth

Brittney C Gannon

Lauren K Johnson

Guanglei Xiong

Renuka Mannem

Tor Hamilton Steiner

Sravan Sripada

Techniques are described here for prompt generation. An example method can include determining, using a first machine learning model, a characteristic of an item based at least in part on a textual description of the item. The method can further include receiving a first input indicating a qualifier describing a theme for the image. The method can further include generating, using the first machine learning model, a prompt for a second machine learning model based at least in part on the characteristic and the background. The method can further include generating, using the second machine learning model, the image based at least in part on the prompt, the image associated with the theme and showing the item and the background.

Text and image based prompt generation

Devices, systems, and methods are provided for generating and selecting video clips for inclusion in video sequences based on still frame images. A method may include encoding first embeddings for a first video including first images of an item at a first scene, the first embeddings indicative of features of the first scene; encoding second embeddings for a second video including second images of the item at a second scene, the second embeddings indicative of features of the second scene; encoding third embeddings for the first video, the third embeddings indicative of features of a first type of camera shot used for the first images; encoding fourth embeddings for the second video, the fourth embeddings indicative of features of a second type of camera shot used for the second images; and generating, based on the first, second, third, and fourth embeddings, a video sequence for the item.

Enhanced generation and selection of video clips from still frame images

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Seattle, WA, United States of America

Yashal Shakti Kanungo