Lemon Inc.

Douyin Vision Co., Ltd.

Kin Chung Wong

Bingchen Liu

Blake Garrett Fuselier

Lingyiqing Zhou

Diana Lee

Chen Sun

Peilin Li

August Yadon

Fan Chen

WeiShan Yu

Zeng Dai

Long Jiang

Lexin Tang

Christopher Juneja

Yuyang Tian

Ari Shapiro

Thomas Oefverstroem

The present disclosure describes techniques for generating content. Text may be received. The text is associated with a video to be created by at least one user. At least one image may be generated based at least in part on the text using at least one machine learning model. The video may be generated based at least in part on the at least one image. The video comprises content overlaid on the at least one image.

Content creation based on text-to-image generation

The present disclosure describes techniques of generating voices for virtual characters. A plurality of source sounds may be received. The plurality of source sounds may correspond to a plurality of frames of a video. The video may comprise a virtual character. The plurality of source sounds may be converted into a plurality of representations in a latent space using a first model. Each representation among the plurality of representations may comprise a plurality of parameters. The plurality of parameters may correspond to a plurality of sound features. A plurality of sounds may be generated in real time for the virtual character in the video based at least in part on modifying at least one of the plurality of parameters of each representation.

Voice generation for virtual characters

Examples are provided relating to media content editing architectures utilizing machine learning techniques. One aspect includes a method for media content editing, the method comprising: receiving a media content from a user; receiving an editing request for the media content from the user; and editing the media content based on the editing request to generate edited media content by: retrieving a prompt from a prompt pool, wherein the retrieved prompt is selected based on the editing request; parsing the retrieved prompt and the editing request using a large language model to generate one or more editing actions to be performed on the media content; and performing the one or more editing actions on the media content to generate the edited media content.

Technical architectures for media content editing using machine learning

The present disclosure provides systems and methods for video generation corresponding to a user input. Given a user input, a story video with content relevant to the user input can be generated. One aspect includes a computing system comprising a processor and memory. The processor can be configured to execute a program using portions of the memory to receive the user input, generate a story text based on the user input, generate a plurality of story images based on the story text, and output a story including the story text and a story video having content corresponding to the story text, wherein the story video includes the plurality of story images. Additionally or alternatively, the story video can include audio data and a plurality of generated animated videos, each animated video corresponding to a story image in the plurality of story images.

Generation of story videos corresponding to user input using generative models

Example aspects include techniques for creating video stickers from user-generated content. These techniques may include receiving selection of a video content item previously uploaded to a SMP server application corresponding to the SMP client application, and presenting a graphical user interface (GUI) for receiving one or more derivative attributes for generating a derivative video sticker from the video content item. In addition, the techniques may include adding the derivative video sticker to a library of stickers provided by the SMP server application to a plurality of SMP client applications, and presenting the derivative video sticker within a communication interface associated with the SMP server application.

Method and system for creating stickers from user-generated content

The present disclosure describes techniques for generating visual effects based on text. The techniques comprise initiating a process of generating a visual effect in response to a selection of an interface element; generating the visual effect, wherein the generating the visual effect comprises generating a first image based on a first text input by a first user, and wherein content of the first image correlates with the first text; and causing to display the first image on a screen of a computing device.

Generation of visual effects based on text

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Los Angeles, CA, United States of America

Kin Chung Wong