Abstract: In some embodiments, a method sends a playlist that includes links to segments of media content. At least a portion of the links include a variable, and a presentation of the media content that is attributable to a request for the media content is created by insertion of values for variables in the playlist. The values are associated with a first version of the media content or a second version of the media content. The method receives a request for a segment and the request contains information from a link included in the playlist. The information includes data based on a value provided for insertion into the link as a substitution for a variable included in the link. The value is associated with the first version of the media content or the second version of the media content. The segment that corresponds to the link is sent.

Abstract: In some embodiments, a method receives input data to calculate an effect of a variable on a group for a plurality of methods. Methods in the plurality of methods calculate the effect of the variable for the input data using different logic. A plurality of sub-weights for methods in the plurality of methods are generated. The sub-weights are generated based on a balance metric, a dissimilarity metric, and a reliability metric. The method combines the plurality of sub-weights for methods in the plurality of methods to generate a final weight for the methods. The respective final weight is applied to an intermediate result from a respective method in the plurality of methods to generate a weighted intermediate result for the method. The method combines weighted intermediate results for the plurality of methods to generate a final result for the effect of the variable.

Abstract: A behavior detection platform may obtain video data of an environment including a guest and may determine a set of normal guest behaviors associated with the environment. The platform may analyze, using a first machine learning model, the video data to identify features of the guest and analyze, using a second machine learning model, the video data and the features to determine an actual guest behavior of the guest. The platform may predict, using the second machine learning model, a predicted guest behavior of the guest based on the actual guest behavior. The behavior detection platform may determine that at least one of the actual guest behavior or the predicted guest behavior is not included in the set of normal guest behaviors. The platform may cause assistance to be provided to the guest based on determining that the actual guest behavior or the predicted guest behavior are not included.

Abstract: In some embodiments, a method receives usage data that is based on a delivery of content by a plurality of content delivery network entities. A first value of a selection parameter is used to determine whether to select a content delivery network entity from the plurality of content delivery network entities to process a first request for content. The method allocates the usage data in a first distribution to the plurality of content delivery network entities. The allocating does not use a condition to determine the first distribution. The usage data is allocated in a second distribution to the plurality of content delivery network entities. The allocating uses the condition to determine the second distribution. The method adjusts the first value of the selection parameter to a second value based on the first distribution and the second distribution.

Abstract: A computer-implemented method of changing a face within an output image or video frame that includes: receiving an input image that includes a face presenting a facial expression in a pose; processing the image with a neural network encoder to generate a latent space point that is an encoded representation of the image; decoding the latent space point to generate an initial output image in accordance with a desired facial identity but with the facial expression and pose of the face in the input image; identifying a feature of the facial expression in the initial output image to edit; applying an adjustment vector to a latent space point corresponding to the initial output image to generate an adjusted latent space point; and decoding the adjusted latent space point to generate an adjusted output image in accordance with the desired facial identity but with the facial expression and pose of the face in the input image altered in accordance with the adjustment vector

Abstract: A computer-implemented method of changing a face within an output image or video frame includes: receiving an input image that includes a face presenting a facial expression in a pose; separately encoding different portions of the image by, for each separately encoded portion, generating a latent space point of the portion, thereby generating a plurality of multi-dimensional vectors where each multi-dimensional vector is an encoded representation of a different portion of the input image; concatenating the plurality of multi-dimensional vectors into a combined latent space vector; and decoding the combined latent space vector to generate the output image in accordance with a desired facial identity but with the facial expression and pose of the face in the input image

Abstract: In some implementations, a controller may cause a robotic arm connector, of the robotic arm, to be connected to a first compartment connector of a passenger compartment of an amusement ride. The controller may cause a second compartment connector, of the passenger compartment, to be disconnected from a first structure connector of a first support structure. The controller may transport the passenger compartment from the first support structure to a second support structure after causing the second compartment connector to be disconnected from the first structure connector.

Abstract: Embodiments of the present disclosure are directed to methods and systems for generating three-dimensional (3D) models and facial hair models representative of subjects (e.g., actors or actresses) using facial scanning technology. Methods accord to embodiments may be useful for performing facial capture on subjects with dense facial hair. Initial subject facial data, including facial frames and facial performance frames (e.g., images of the subject collected from a capture system) can be used to accurately predict the structure of the subject’s face underneath their facial hair to produce a reference 3D facial shape of the subject. Likewise, image processing techniques can be used to identify facial hairs and generate a reference facial hair model. The reference 3D facial shape and reference facial hair mode can subsequently be used to generate performance 3D facial shapes and a performance facial hair model corresponding to a performance by the subject (e.g., reciting dialog).

Abstract: Methods and systems for performing facial retargeting using a patch-based technique are disclosed. One or more three-dimensional (3D) representations of a source character's (e.g., a human actor's) face can be transferred onto one or more corresponding representations of a target character's (e.g., a cartoon character's) face, enabling filmmakers to transfer a performance by a source character to a target character. The source character's 3D facial shape can separated into patches. For each patch, a patch combination (representing that patch as a combination of source reference patches) can be determined. The patch combinations and target reference patches can then be used to create target patches corresponding to the target character. The target patches can be combined using an anatomical local model solver to produce a 3D facial shape corresponding to the target character, effectively transferring a facial performance by the source character to the target character.

Abstract: Embodiments of the present disclosure are directed to methods and systems for generating three-dimensional (3D) models and facial hair models representative of subjects (e.g., actors or actresses) using facial scanning technology. Methods accord to embodiments may be useful for performing facial capture on subjects with dense facial hair. Initial subject facial data, including facial frames and facial performance frames (e.g., images of the subject collected from a capture system) can be used to accurately predict the structure of the subject's face underneath their facial hair to produce a reference 3D facial shape of the subject. Likewise, image processing techniques can be used to identify facial hairs and generate a reference facial hair model. The reference 3D facial shape and reference facial hair mode can subsequently be used to generate performance 3D facial shapes and a performance facial hair model corresponding to a performance by the subject (e.g., reciting dialog).

Abstract: In some implementations, a device may provide a data structure storing first data, wherein the first data indirectly identifies second data, and wherein the second data identifies a particular individual. The device may obtain, from the data structure, the first data. The device may generate a cryptographically random value using a secure generator. The device may combine the first data and the cryptographically random value to generate hashing input data. The device may perform, using a hashing algorithm, a hashing operation on the hashing input data to generate de-identified first data, wherein re-identification of the de-identified first data requires knowledge of at least the first data, the cryptographically random value, and the de-identified first data. The device may perform an action using the de-identified first data.

Abstract: Restoration methods and systems are disclosed for video remastering. Techniques disclosed include receiving a video sequence. For each frame of the video sequence, techniques disclosed include encoding, by a degradation encoder, a video content associated with the frame into a latent vector. The latent vector is a representation of the degradation present in the video content; the degradation present in the video content includes one or more degradation types. Based on the latent vector and the video content, techniques disclosed further include generating, by a backbone network, one or more feature maps, and, then, restoring the frame based on the one or more feature maps.

Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.

Abstract: Some implementations of the disclosure are directed to a method, comprising: receiving tapped data that was tapped from a controller of an industrial control system (ICS) while the controller executed first control code to control ICS devices of the ICS, the tapped data used during execution of the first control code, and the tapped data comprising input data obtained from one or more input components of the controller communicatively coupled to the ICS devices, or output data obtained from one or more output components of the controller communicatively coupled to the ICS devices; and after receiving the tapped data, executing, using at least the tapped data, second control code that provides an emulation of the controller, the emulation comprising running, using at least the tapped data, a process of the first control code at a faster rate than it is run by the controller executing the first control code.

Abstract: Some implementations of the disclosure are directed to capturing facial training data for one or more subjects, the captured facial training data including each of the one or more subject's facial skin geometry tracked over a plurality of times and the subject's corresponding jaw poses for each of those plurality of times; and using the captured facial training data to create a model that provides a mapping from skin motion to jaw motion. Additional implementations of the disclosure are directed to determining a facial skin geometry of a subject; using a model that provides a mapping from skin motion to jaw motion to predict a motion of the subject's jaw from a rest pose given the facial skin geometry; and determining a jaw pose of the subject using the predicted motion of the subject's jaw.

Abstract: A method of providing configurable control of voice command systems is disclosed herein. The method comprising a parent device receiving a voice command and comparing the voice command to a set of preferences for a user. The set of preferences may relate to one or more interactions between the user and a playback device. The method further comprises the parent device translating the voice command to a user interface (UI) command sequence for the playback device based on the comparison. The playback device may then send the UI command sequence to the playback device as the configurable voice command. The playback device executes the UI command sequence to transition from the current output state to the desired output state intended by the user. A system for providing configurable voice control and computer program product are also disclosed.

Abstract: Systems and methods for an interactive communications system capable of generating a response to conversational input are provided. The interactive communications system analyzes the conversational input to determine relevant topics of discussion. The interactive communications system further determines which of the relevant topics of discussion can potentially lead to an unwanted end to a conversation. The interactive communications system redirects the conversation by providing responses to the conversational input that are intended simply to avoid the unwanted end to the conversation.

Abstract: A method of generating a training data set for training an image matting machine learning model includes receiving a plurality of foreground images, generating a plurality of com posited foreground images by com positing randomly selected foreground images from the plurality of foreground images, and generating a plurality of training images by compositing each composited foreground image with a randomly selected background image. The training data set includes the plurality of training images.

Abstract: Systems and methods for simulation of ambient light based on generated imagery are disclosed herein. Such a system can include a simulation sled, a simulation display that can display generated imagery viewable from the simulation sled, an ambient light simulator that can selectively illuminate portions of the simulation sled, and a processor. The simulation sled can include a plurality of user controls. The processor can: control the simulation display to generate imagery; identify an effect of the generated imagery on the simulation sled; and control the ambient light simulator to selectively illuminate at least portions of the simulation sled according to the identified effect of the simulated light source.

Abstract: Some implementations of the disclosure are directed to tapping input/output (I/O) data from an industrial control system (ICS) or applying the tapped I/O data to a learned model to perform predictive or prescriptive maintenance. In one implementation, a method comprises: tapping I/O data from a controller of an ICS while the controller executes first control code to control one or more devices of the ICS; transmitting the tapped I/O data over a network to a second system; and executing, via the second system, second control code comprising an original or modified version of all or a subset of the first control code of the ICS, wherein the second control code executes in response to receiving the tapped I/O data. The output of executing the second control code may be provided to a model to predict a future event involving the ICS or to prescribe maintenance of the ICS.

Abstract: A system comprises active magnetic emitters positioned within an area, passive magnetic emitters configured to be moved within the area, a magnetic field detector configured to measure a strength and direction of a magnetic field within the area, and a processor in communication with the magnetic field detector. The passive magnetic emitters are configured to be integrated in, coupled to, or secured to at least one tracked object or tracked subject within the area. The processor is configured to evaluate at least one change in the measured strength and direction of the magnetic field end send a signal to a visual effect actuator or visual effects display to initiate a visual effect based on the at least one change. A method and computer program product relating to the system is also provided.

Abstract: Systems and methods are disclosed for reconstructing a frame. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining one or more reference frames from non-transient electronic storage, generating one or more displacement maps based on the one or more reference frames and a target frame with the physical computer processor, generating one or more warped frames based on the one or more reference frames and the one or more displacement maps with the physical computer processor, obtaining a conditioned reconstruction model from the non-transient electronic storage, and generating one or more blending coefficients and one or more reconstructed displacement maps by applying the one or more displacement maps, the one or more warped frames, and a target frame to the conditioned reconstruction model with the physical computer processor.

Abstract: Systems and methods are disclosed for generating a latent space residual. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining a target frame, obtaining a reconstructed frame, encoding the target frame into a latent space to generate a latent space target frame, encoding the reconstructed frame into the latent space to generate a latent space reconstructed frame, and generating a latent space residual based on the latent space target frame and the latent space reconstructed frame.

Abstract: Systems and methods are disclosed for partitioning an animatable model. The system may include a non-transitory computer-readable medium operatively coupled to processors. The non-transitory computer-readable medium may store instructions that, when executed, cause the processors to perform a number of operations. One operation may include obtaining an animatable model. The animatable model may include a geometry matrix. Another operation may include obtaining user input to partition an animatable model into multiple partitions. The user input may include a connected curve in the geometry matrix. Yet another operation may include partitioning the animatable model into the multiple partitions based on the user input.

Abstract: Systems, methods, and devices disclosed herein relate to an improved interactive experience. The system can be for delivery of an interactive attraction experience. The system can include a passenger vehicle having a plurality of passenger locations and a content presentation system that can present a virtual portion of the attraction experience viewable from the plurality of passenger locations. The system can include a transceiver that can transmit a trigger signal and at least one processor. The at least one processor can control delivery of the virtual portion of the attraction experience via the content presentation system, detect presence of a non-ride device, and deliver a trigger signal via the transceiver to the non-ride device, the trigger signal linked with the attraction experience.

Abstract: Systems and methods for dynamic modification of an amusement ride are disclosed herein. The system can include a simulation vehicle including a plurality of controls and at least one interface. The system can include a content presentation system, and a processor. The processor can: provide content to the at least one passenger; identify a user skill level based on a plurality of user inputs received by at least some of the plurality of controls of the simulation vehicle; identify a modification to a difficulty of the ride experience based in part on the identified user skill level; and modify the difficulty of the ride experience according to the identified modification.

Abstract: Systems and methods are described for dynamically adjusting an amount of retrieved recognition data based on the needs of a show, experience, or other event where participants are recognized. The retrieved recognition data may be deleted once it is no longer needed for the event. Recognition data retrieval is limited to just what is needed for the particular task, minimizing the uniqueness of any retrieved recognition data to respect participant privacy while providing an enhanced participant experience through recognition.

Abstract: Some implementations of the disclosure are directed to capturing facial training data for one or more subjects, the captured facial training data including each of the one or more subject's facial skin geometry tracked over a plurality of times and the subject's corresponding jaw poses for each of those plurality of times; and using the captured facial training data to create a model that provides a mapping from skin motion to jaw motion. Additional implementations of the disclosure are directed to determining a facial skin geometry of a subject; using a model that provides a mapping from skin motion to jaw motion to predict a motion of the subject's jaw from a rest pose given the facial skin geometry; and determining a jaw pose of the subject using the predicted motion of the subject's jaw.

Abstract: Embodiments disclosed herein include an amusement park ride. The amusement park ride include a ride vehicle that can transition from a first configuration to a second configuration during the duration of the ride. The amusement park ride can include a loading area for loading passengers into the ride vehicle and a separate unloading area for disembarking passengers from the ride vehicle. After disembarking passengers from the ride vehicle the ride vehicle travels to a transition area concealed from the public where the ride transitions from the second configuration to the first configuration. In the transition area, maintenance can be performed on the ride vehicle and calibration can be performed on one or more of the ride vehicle systems (e.g., projector or audio systems).

Abstract: Systems, methods, and devices disclosed herein relate to an improved interactive experience. The system can be for delivery of an interactive attraction experience. The system can include a passenger vehicle having a plurality of passenger locations and a content presentation system that can present a virtual portion of the attraction experience viewable from the plurality of passenger locations. The system can include a transceiver that can transmit a trigger signal and at least one processor. The at least one processor can control delivery of the virtual portion of the attraction experience via the content presentation system, detect presence of a non-ride device, and deliver a trigger signal via the transceiver to the non-ride device, the trigger signal linked with the attraction experience.

Abstract: The present disclosure may be embodied in systems, methods, and computer readable media, and may allow for interactive responses by network-enabled objects, programs, and machines. Embodiments described are well-suited for communicating and responding using small-sized data messages, thus allowing for their implementation in standard messaging systems and by simple devices such as toys and other low-level electronic devices that may have limited processing capacity and/or memory. The present disclosure provides in one embodiment a method comprising receiving at least one broadcast message, each broadcast message in the at least one broadcast message comprising a plurality of identifiers and determining a highest priority identifier amongst the plurality of identifiers received in the at least one broadcast message. The method may further comprise identifying a command sequence associated with the highest priority identifier and executing the command sequence.

Abstract: Some implementations of the disclosure are directed to allowing interactive broadcast streamed video from games and other dynamic content. In accordance with some implementations, a content creator may publish a plurality of video surfaces of an environment for streaming to a plurality of client devices for video playback. The plurality of video surfaces may correspond, for example, to a cube map of a gaming environment captured from the perspective of a player. Upon receiving a stream including multiple video surfaces such as a cubemap, a media player of a viewer may generate a fully-rendered three-dimensional view of the environment.

Abstract: Systems and methods are disclosed for modifying labeled target content for a capture device. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining labeled target content, the labeled target content including one or more facial features that have been labeled; modifying the labeled target content to match dynamically captured content from a first capture device to generate modified target content; and storing the modified target content. The dynamically captured content may include the one or more facial features.

Abstract: A system and method for high-quality, three-dimensional live streaming of graphical content. Graphical geometry data generated through execution of multimedia software, e.g. a video game, is converted into a geometry streaming format that is independent of the operating system of the host system running the software. The converted data is broadcast over a network to one or more spectator systems, which are configured to execute the converted data to render locally the three-dimensional content. A spectator is therefore enabled to change a viewing angle within the three-dimensional content regardless of the viewing angle associated with the host system.

Abstract: Systems and methods for predicting a target set of pixels are disclosed. In one embodiment, a method may include obtaining target content. The target content may include a target set of pixels to be predicted. The method may also include convolving the target set of pixels to generate an estimated set of pixels. The method may include matching a second set of pixels in the target content to the target set of pixels. The second set of pixels may be within a distance from the target set of pixels. The method may include refining the estimated set of pixels to generate a refined set of pixels using a second set of pixels in the target content.

Abstract: Some implementations of the disclosure are directed to automatically rotating displays to display media content based on metadata extracted from the media content that provides an indication of a target display orientation to display the media content. In one implementation, a method includes: detecting media content for display on a display, wherein the display is mounted on a rotatable display mount; extracting metadata from the detected media content, the extracted metadata providing an indication of a target display orientation to display the media content; using at least the extracted metadata, automatically causing the rotatable display mount to rotate the display to the target orientation; and displaying the media content on the rotated display.

Abstract: One or more embodiments of the present disclosure include a system for providing dynamic virtual reality ground effects. The system includes a user interface surface and multiple motors coupled to the user interface surface. At least one of the motors is coupled to a virtual reality component of an electronic device. A first motor of the multiple motors is driven by movement of the user interface surface and is used to generate a feedback electrical signal in response to the movement of the user interface surface. A second motor of the multiple motors is driven using the feedback electrical signal.

Abstract: A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: Systems, methods, and devices are disclosed for tracking physical objects using a passive reflective object. A computer-implemented method includes obtaining a location profile derived from content capturing a passive object having a reflective surface reflecting one or more real-world objects. The passive object is attached to a physical object. The method further includes transmitting the location profile to a simulation device. The method further includes generating a virtual representation of the physical object based on the location profile of the passive object. The method further includes presenting the virtual representation in a simulation experience.

Abstract: Systems and methods for compressing target content are disclosed. In one embodiment, a system may include non-transient electronic storage and one or more physical computer processors. The one or more physical computer processors may be configured by machine-readable instructions to obtain the target content comprising one or more frames, wherein a given frame comprises one or more features. The one or more physical computer processors may be configured by machine-readable instructions to obtain a conditioned network. The one or more physical computer processors may be configured by machine-readable instructions to generate decoded target content by applying the conditioned network to the target content.

Abstract: Embodiments of the present disclosure techniques for modeling and capturing the dynamic appearance of skin. These techniques can couple dynamic reflectance parameters for skin (albedo and specular reflectance) with dynamic geometry. The disclosed techniques allow for capture and modeling of the dynamic appearance of skin for an actor. The techniques can re-render the actor's face accurately to accurately model the appearance of skin including the albedo of skin that can change primarily due to blood flow. The techniques can also re-render the actor's face accurately under multiple different lighting conditions.

Abstract: Systems and methods are disclosed for compressing a target video. A computer-implemented method may use a computer system that include one or more physical computer processors and non-transient electronic storage. The computer-implemented method may include: obtaining the target video, extracting one or more frames from the target video, and generating an estimated optical flow based on a displacement of pixels between the one or more frames. The one or more frames may include one or more of a key frame and a target frame.

Abstract: The disclosure describes a high dynamic range video coding pipeline that may reduce color artifacts and improve compression efficiency. The disclosed pipeline separates the luminance component from the chrominance components of an input signal (e.g., an RGB source video) and applies a scaling of the chrominance components before encoding, thereby reducing perceivable color artifacts while maintaining luminance quality.

Abstract: Supervised machine learning using convolutional neural network (CNN) is applied to denoising images rendered by MC path tracing. The input image data may include pixel color and its variance, as well as a set of auxiliary buffers that encode scene information (e.g., surface normal, albedo, depth, and their corresponding variances). In some embodiments, a CNN directly predicts the final denoised pixel value as a highly non-linear combination of the input features. In some other embodiments, a kernel-prediction neural network uses a CNN to estimate the local weighting kernels, which are used to compute each denoised pixel from its neighbors. In some embodiments, the input image can be decomposed into diffuse and specular components. The diffuse and specular components are then independently preprocessed, filtered, and postprocessed, before recombining them to obtain a final denoised image.

Abstract: Implementations of the disclosure describe AI systems that offer an improvisational story telling AI agent that may interact collaboratively with a user. In one implementation, a story telling device may be implemented using i) a natural language understanding (NLU) component to process human language input (e.g., digitized speech or text input); ii) a natural language processing (NLP) component to parse the human language input into a story segment or sequence; iii) a component for storing/recording the story as it is created by collaboration; iv) a component for generating AI-suggested story elements; and v) a natural language generation (NLG) component to transform the AI-generated story segment into natural language that may be presented to the user.

Abstract: One particular implementation may take the form of a method and system for decoding 3-D multimedia content. In one example, the present invention may take the form of a method of decoding 3-D multimedia content encoded with at least content frames and null frames. The method may operate to receive the 3-D multimedia content, extract at least one content frame, at least one null frame, and display information. The method may further operate to reconstruct the content and null frames in a display-sequential manner using the display information to indicate the display order of the at least one content frame. In some examples, the method and system for encoding and decoding multimedia content may encode the multimedia content such that the at least one null frame may not be displayed by a display device.

Abstract: Systems and methods are provided for presenting visual media on a structure having a plurality of unordered light sources, e.g., fiber optic light sources, light emitting diodes (LEDs), etc. Visual media can be created based on a computer model of the structure. Images of the structure can be analyzed to determine the location of each of the light sources. A lookup table can be generated based on the image analysis, and used to correlate pixels of the visual media to one or more of the actual light sources. A visual media artist or designer need not have prior knowledge of the order/layout of the light sources on the structure in order to create visual media to be presented thereon.

Abstract: Systems, devices, and methods are disclosed for presenting an interactive narrative. An apparatus includes a user interface. The apparatus also includes one or more processors operatively coupled to the user interface and a non-transitory computer-readable medium. The non-transitory computer-readable medium stores instructions that, when executed, cause the one or more processors to present a first piece of content corresponding to a given narrative via the user interface. The given narrative includes one or more characteristics. The one or more processors are caused to receive user input via the user interface. The one or more processors are caused to classify the user input into one of a plurality of response models. The one or more processors are caused to dynamically respond to the user input by presenting a second piece of content. The second piece of content is based on a selected response model corresponding to the user input.