Adobe, Inc.

Timothy Langlois

Li-Yi Wei

Dingzeyu Li

Nicholas J Bryan

James P Corbett

Kazi Rubaiat Habib

Christopher Nuuja

John Simpson

Zhenyu Tang

Brooke Hopper

Oliver Wang

Pedro Morgado

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and modifying digital animations based on user interactions with a unique user interface portraying a one-dimensional layer motion element and/or elements for generating and utilizing animation path for digital design objects and animation layers. The disclosed system can provide a dynamic one-dimensional layer motion element that adapts to a selected animation layer and portrays selectable animation frames from the animation layer. The disclosed systems can provide options for generating and modifying various frames of the digital animation based on user interactions with the one-dimensional layer motion element, an animation timeline, and/or a corresponding animation canvas. Additionally, in some embodiments, the disclosed systems also generate path animations with complex animation effects based on user selection of animation paths, digital design objects of animation layers, and corresponding selectable path animation feature tools.

Dynamic path animation of animation layers and digital design objects

Providing and utilizing a one-dimensional layer motion element to generate and manage digital animations

Methods, systems, and non-transitory computer readable storage media are disclosed for rendering scene-aware audio based on acoustic properties of a user environment. For example, the disclosed system can use neural networks to analyze an audio recording to predict environment equalizations and reverberation decay times of the user environment without using a captured impulse response of the user environment. Additionally, the disclosed system can use the predicted reverberation decay times with an audio simulation of the user environment to optimize material parameters for the user environment. The disclosed system can then generate an audio sample that includes scene-aware acoustic properties based on the predicted environment equalizations, material parameters, and an environment geometry of the user environment. Furthermore, the disclosed system can augment training data for training the neural networks using frequency-dependent equalization information associated with measured and synthetic impulse responses.

Generating scene-aware audio using a neural network-based acoustic analysis

Rendering scene-aware audio using neural network-based acoustic analysis

Certain embodiments involve generating and providing spatial audio using a predictive model. For example, a generates, using a predictive model, a visual representation of visual content provideable to a user device by encoding the visual content into the visual representation that indicates a visual element in the visual content. The system generates, using the predictive model, an audio representation of audio associated with the visual content by encoding the audio into the audio representation that indicates an audio element in the audio. The system also generates, using the predictive model, spatial audio based at least in part on the audio element and associating the spatial audio with the visual element. The system can also augment the visual content using the spatial audio by at least associating the spatial audio with the visual content.

Generating spatial audio using a predictive model

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Seattle, WA, United States of America

Timothy Langlois