Abstract: 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. However, techniques are described for modifying an image or audio or both of the sky box responsive to identifying the character is moving toward the sky box.

Abstract: Automatically recommending sound effects based on visual scenes enables sound engineers during video production of computer simulations, such as movies and video games. This recommendation engine may be accomplished by classifying SFX and using a machine learning engine to output a first of the classified SFX for a first computer simulation based on learned correlations between video attributes of the first computer simulation and the classified SFX.

Abstract: Methods and systems for representing emotions of an audience of spectators viewing online gaming of a video game include capturing interaction data from spectators in an audience engaged in watching gameplay of the video game. The captured interaction data is aggregated by clustering the spectators into different groups in accordance to emotions detected from the spectators in the audience. An avatar is generated to represent emotion of each group and expressions of the avatar are dynamically adjusted to match changes in the expressions of the spectators of the respective group. The avatars representing distinct emotions of different group of spectators is presented alongside content of the video game. A size of the avatar for each distinct emotion is influenced by the confidence score associated with the respective group of spectators.

Abstract: 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.

Abstract: 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 visual, a positive audio signal, and a negative audio signal. A trained Sound Recommendation Network is configured to output an audio embedding and a visual embedding and use the audio embedding and visual embedding to compute a correlation distance between an image frame or video segment and one or more audio segments retrieved from a database. The correlation distances for the one or more audio segments in the database are sorted and one or more audio segments with the closest correlation distance from the sorted audio correlation distances are determined. The audio segment with the closest audio correlation distance is applied to the input image frame or video segment.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems for representing emotions of an audience of spectators viewing online gaming of a video game include capturing interaction data from spectators in an audience engaged in watching gameplay of the video game. The captured interaction data is aggregated by clustering the spectators into different groups in accordance to emotions detected from the spectators in the audience. An avatar is generated to represent emotion of each group and expressions of the avatar are dynamically adjusted to match changes in the expressions of the spectators of the respective group. The avatars representing distinct emotions of different group of spectators is presented alongside content of the video game. A size of the avatar for each distinct emotion is influenced by the confidence score associated with the respective group of spectators.

Abstract: An automated system that improves the spectating experience for a computer game or e-sport by placing spectators in locations based on their preferences, interests, demographics, and changes in emotions or behavior as the game progresses. The spectator may be moved automatically or may elect to move within the virtual space to improve the immersive experience. In some cases, the system may charge for better spectating experience. The system also detects abusive and inappropriate spectator behavior and allows such behavior to be isolated, in order to improve the spectating experience.

Abstract: 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.

Abstract: 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.

Abstract: 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 visual, a positive audio signal, and a negative audio signal. A trained Sound Recommendation Network is configured to output an audio embedding and a visual embedding and use the audio embedding and visual embedding to compute a correlation distance between an image frame or video segment and one or more audio segments retrieved from a database. The correlation distances for the one or more audio segments in the database are sorted and one or more audio segments with the closest correlation distance from the sorted audio correlation distances are determined. The audio segment with the closest audio correlation distance is applied to the input image frame or video segment.

Abstract: 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.

Abstract: Sound effects (SFX) are registered in a database for efficient search and retrieval. This may be accomplished by classifying SFX and using a machine learning engine to output a first of the classified SFX for a first computer simulation based on learned correlations between video attributes of the first computer simulation and the classified SFX. Subsequently, videos without sound may be processed for object, action, and caption recognition to generate video tags which are semantically matched with SFX tags to associate SFX with the video.

Abstract: An automated system that improves the spectating experience for a computer game or e-sport by placing spectators in locations based on their preferences, interests, demographics, and changes in emotions or behavior as the game progresses. The spectator may be moved automatically or may elect to move within the virtual space to improve the immersive experience. In some cases, the system may charge for better spectating experience. The system also detects abusive and inappropriate spectator behavior and allows such behavior to be isolated, in order to improve the spectating experience.

Abstract: 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 receives a first image frame from a host system and applies a style adapted from a second image frame to the first image frame to generate a style adapted first image frame.

Abstract: Automatically recommending sound effects based on visual scenes enables sound engineers during video production of computer simulations, such as movies and video games. This recommendation engine may be accomplished by classifying SFX and using a machine learning engine to output a first of the classified SFX for a first computer simulation based on learned correlations between video attributes of the first computer simulation and the classified SFX.

Abstract: 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.

Abstract: Automatically recommending sound effects based on visual scenes enables sound engineers during video production of computer simulations, such as movies and video games. This recommendation engine may be accomplished by classifying SFX and using a machine learning engine to output a first of the classified SFX for a first computer simulation based on learned correlations between video attributes of the first computer simulation and the classified SFX.

Abstract: 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.