Sony Interactive Entertainment Inc.

Sudha Krishnamurthy

Mahdi Azmandian

Michael G Taylor

Masanori Omote

Justice Adams

Arindam Jati

David Nelson

Michael Taylor

Xiaoyu Liu

Naveen Kumar

Ashish Singh

Celeste Bean

Sarah Karp

Victoria Dorn

Sepideh Karimi

Kristie Ramirez

Jin Zhang

Jian Zheng

A computer simulation object such as a chair is described by voice or photo input to render a 2D image. Machine learning may be used to convert voice input to the 2D image. The 2D image is converted to a 3D asset and the 3D asset or portions thereof are used in the computer simulation, such as a computer game, as the object such as a chair.

Voice driven modification of sub-parts of assets in computer simulations

Methods and systems for representing emotions of an audience of spectators viewing online gaming of a video game include capturing interaction data from spectators of an audience engaged in watching gameplay of the video game. The captured interaction data is used to cluster the spectators into different groups in accordance to emotions detected from the interactions of spectators in the audience. A reaction track is identified for each group based on the emotion associated with the group. The reaction tracks representing distinct emotions of different groups of spectators are presented alongside content of the video game.

Reaction track for different spectator groups within an audience

The present disclosure generally relates to providing virtual education and education to a user. More specifically, the present system relates to educating and onboarding spectators of electronic sports (eSports) events. The onboarding activities are used to further engage the spectators with the eSports event in general, as well as the game played during the eSports event. In other aspects, the eSports onboarding activity may be modified based on the type of game being played, the user's experience with the specific game or game genre, and other user preferences.

Esports spectator onboarding

A Sound effect recommendation network is trained using a machine learning algorithm with a reference image, a positive audio embedding and a negative audio embedding as inputs to train a visual-to-audio correlation neural network to output a smaller distance between the positive audio embedding and the reference image than the negative audio embedding and the reference image. The visual-to-audio correlation neural network is trained to identify one or more visual elements in the reference image and map the one or more visual elements to one or more sound categories or subcategories within an audio database.

Training a sound effect recommendation network

Systems and methods for audio processing are described. An audio processing system receives audio content that includes a voice sample. The audio processing system analyzes the voice sample to identify a sound type in the voice sample. The sound type corresponds to pronunciation of at least one specified character in the voice sample. The audio processing system generates a filtered voice sample at least in part by filtering the voice sample to modify the sound type. The audio processing system outputs the filtered voice sample.

Systems and methods for automated customized voice filtering

Deep learning is used to dynamically adapt virtual humans in metaverse applications. The adaptation can be according to user preferences. In addition or alternatively, virtual humans and pets can be adapted for metaverse applications based on demographics of the user. The user's personal demographics may be used to establish the costume, skin color, emotion, voice, and behavior of the virtual humans. Similar considerations may be used to adapt virtual pets to the user's experience of the metaverse.

Customized digital humans and pets for meta verse

A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.

Personalized data driven game training system

A computer simulation object such as a chair is described by voice or photo input to render a 2D image. Machine learning may be used to convert voice input to the 2D image. The 2D image is converted to a 3D object and the 3D object or portions thereof are used in the computer simulation, such as a computer game, as the object such as a chair. A physics engine can be used to modify the 3D objects.

Voice driven modification of physical properties and physics parameterization in a closed simulation loop for creating static assets in computer simulations

Sound effect recommendations for visual input are generated by training machine learning models that learn coarse-grained and fine-grained audio-visual correlations from a reference image, a positive audio signals, and a negative audio signal. A positive audio embedding related to the reference image is generated from the positive audio signal and a negative audio embedding is generated from a negative audio signal. A machine learning algorithm uses the reference image, the positive audio embedding and the negative audio embedding as inputs to train a visual-to-audio correlation neural network to output a smaller distance between the positive audio embedding and the reference image than the negative audio embedding and the reference image.

Self-supervised AI-assisted sound effect recommendation for silent video

A character in a game world of a computer simulation is identified as moving toward a sky box in the simulation. The computer simulation does not permit simulation characters to enter the sky box. Techniques are described for remastering the skybox based on various parameters.

Create and remaster computer simulation skyboxes

A system enhances existing audio-visual content with audio describing the setting of the visual content. A scene annotation module classifies scene elements from an image frame received from a host system and generates a caption describing the scene elements. A text to speech synthesis module may then convert the caption to synthesized speech data describing the scene elements within the image frame

Scene annotation using machine learning

Graphical style modification may be implemented using machine learning. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. A Graphical Style Modification module generates a style adapted video stream by applying a style adapted from a target image frame to each image frame in a buffered video stream.

Graphical style modification for video games using machine learning

An automated method, system, and computer readable medium for generating sound effect recommendations for visual input by training machine learning models that learn audio-visual correlations from a reference image or video, a positive audio signal, and a negative audio signal. A machine learning algorithm is used with a reference visual input, a positive audio signal input or a negative audio signal input to train a multimodal clustering neural network to output representations for the visual input and audio input as well as correlation scores between the audio and visual representations. The trained multimodal clustering neural network is configured to learn representations in such a way that the visual representation and positive audio representation have higher correlation scores than the visual representation and a negative audio representation or an unrelated audio representation.

Self-supervised AI-assisted sound effect generation for silent video using multimodal clustering

Methods and systems for representing emotions of an audience of spectators viewing online gaming of a video game include capturing interaction data from spectators of an audience engaged in watching gameplay of the video game. The captured interaction data is used to cluster the spectators into different groups in accordance to emotions detected from the interactions of spectators in the audience. A graphic interchange format file (GIF) is identified for each group based on the emotion associated with the group. The GIFs representing the distinct emotions of different groups of spectators are forwarded to client devices of spectators for rendering alongside content of the video game.

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Foster City, CA, United States of America

Sudha Krishnamurthy