Abstract: This disclosure presents systems and methods to provide an interactive environment in response to touch-based inputs. A first body channel communication device coupled to a user may transmit and/or receive signals configured to be propagated along skin of the user such that the skin of the user comprises a signal transmission path. A second body channel communication device coupled to an interaction entity may be configured to transmit and/or receive signals configured to be propagated along the skin of the user along the signal transmission path. A presentation device may present images of virtual content to the user. Information may be communicated between the first body channel communication device, the second body channel communication device, and the presentation device so that virtual content specific to the interaction entity may be presented to augment an appearance of the interaction entity.

Abstract: In particular embodiments, a 2D representation of an object may be provided. A first method may comprise: receiving sketch input identifying a target position for a specified portion of the object; computing a deformation for the object within the context of a character rig specification for the object; and displaying an updated version of the object. A second method may comprise detecting sketch input; classifying the sketch input, based on the 2D representation, as an instantiation of the object; instantiating the object using a 3D model of the object; and displaying a 3D visual representation of the object.

Abstract: According to one implementation, a pose estimation and body tracking system includes a computing platform having a hardware processor and a system memory storing a software code including a tracking module trained to track motions. The software code receives a series of images of motion by a subject, and for each image, uses the tracking module to determine locations corresponding respectively to two-dimensional (2D) skeletal landmarks of the subject based on constraints imposed by features of a hierarchical skeleton model intersecting at each 2D skeletal landmark. The software code further uses the tracking module to infer joint angles of the subject based on the locations and determine a three-dimensional (3D) pose of the subject based on the locations and the joint angles, resulting in a series of 3D poses. The software code outputs a tracking image corresponding to the motion by the subject based on the series of 3D poses.

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: The present disclosure relates to techniques for reconstructing an object in three dimensions that is captured in a set of two-dimensional images. The object is reconstructed in three dimensions by computing depth values for edges of the object in the set of two-dimensional images. The set of two-dimensional images may be samples of a light field surrounding the object. The depth values may be computed by exploiting local gradient information in the set of two-dimensional images. After computing the depth values for the edges, depth values between the edges may be determined by identifying types of the edges (e.g., a texture edge, a silhouette edge, or other type of edge). Then, the depth values from the set of two-dimensional images may be aggregated in a three-dimensional space using a voting scheme, allowing the reconstruction of the object in three dimensions.

Abstract: According to one implementation, a system includes a computing platform having a hardware processor and a system memory storing a software code including multiple artificial neural networks (ANNs). The hardware processor executes the software code to partition a multi-dimensional input vector into a first vector data and a second vector data, and to transform the second vector data using a first piecewise-polynomial transformation parameterized by one of the ANNs, based on the first vector data, to produce a transformed second vector data. The hardware processor further executes the software code to transform the first vector data using a second piecewise-polynomial transformation parameterized by another of the ANNs, based on the transformed second vector data, to produce a transformed first vector data, and to determine a multi-dimensional output vector based on an output from the plurality of ANNs.

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: The present disclosure relates to an apparatus, system and method for processing transmedia content data. More specifically, the disclosure provides for identifying and inserting one item of media content within another item of media content, e.g. inserting a video within a video, such that the first item of media content appears as part of the second item. The invention involves analysing a first visual media item to identify one or more spatial locations to insert the second visual media item within the image data of the first visual media item, detecting characteristics of the one or more identified spatial locations, transforming the second visual media item according to the detected characteristics and combining the first visual media item and second visual media item by inserting the transformed second visual media item into the first visual media item at the one or more identified spatial locations.

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: A system and method for non-invasive reconstruction of an entire object-specific or person-specific teeth row from just a set of photographs of the mouth region of an object (e.g., an animal) or a person (e.g., an actor or a patient) are provided. A teeth statistic model defining individual teeth in a teeth row can be developed. The teeth statistical model can jointly describe shape and pose variations per tooth, and as well as placement of the individual teeth in the teeth row. In some embodiments, the teeth statistic model can be trained using teeth information from 3D scan data of different sample subjects. The 3D scan data can be used to establish a database of teeth of various shapes and poses. Geometry information regarding the individual teeth can be extracted from the 3D scan data. The teeth statistic model can be trained using the geometry information regarding the individual teeth.

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: Systems and methods for distortion removal at multiple quality levels are disclosed. In one embodiment, a method may include receiving training content. The training content may include original content, reconstructed content, and training distortion quality levels corresponding to the reconstructed content. The reconstructed content may be derived from distorted original content. The method may also include training distortion quality levels corresponding to the reconstructed content. The method may further include receiving an initial distortion removal model. The method may include generating a conditioned distortion removal model by training the initial distortion removal model using the training content. The method may further include storing the conditioned distortion removal model.

Abstract: A recommender engine is configured to access memory and surface transmedia content items; and/or linked transmedia content subsets; and/or one or more identifications of identified users; and/or content items of the plurality of transmedia content items associated with at least one identified user. The surfaced items are presented for selection by the given user via the transmedia content linking engine as one or more user-selected transmedia content items.

Abstract: Enhanced removing of noise and outliers from one or more point sets generated by image-based 3D reconstruction techniques is provided. In accordance with the disclosure, input images and corresponding depth maps can be used to remove pixels that are geometrically and/or photometrically inconsistent with the colored surface implied by the input images. This allows standard surface reconstruction methods (such as Poisson surface reconstruction) to perform less smoothing and thus achieve higher quality surfaces with more features. In some implementations, the enhanced point-cloud noise removal in accordance with the disclosure can include computing per-view depth maps, and detecting and removing noisy points and outliers from each per-view point cloud by checking if points are consistent with the surface implied by the other input views.

Abstract: Methods, systems, and computer-readable memory are provided for determining time-varying anatomical and physiological tissue characteristics of an animation rig. For example, shape and material properties are defined for a plurality of sample configurations of the animation rig. The shape and material properties are associated with the plurality of sample configurations. An animation of the animation rig is obtained, and one or more configurations of the animation rig are determined for one or more frames of the animation. The determined one or more configurations include shape and material properties, and are determined using one or more sample configurations of the animation rig. A simulation of the animation rig is performed using the determined one or more configurations. Performing the simulation includes computing physical effects for addition to the animation of the animation rig.

Abstract: The invention relates to systems and methods for manipulating non-linearly connected transmedia content, in particular for creating, processing and/or managing non-linearly connected transmedia content and for tracking content creation and attributing transmedia content to one or more creators. Specifically, the invention involves creating a transmedia content data item by a first user and storing the transmedia content data item in a data store, along with a record indicating an association between the first user and the transmedia content data item; creating an ordered group of transmedia content data items by a second user, the ordered group comprising a pointer to the transmedia content data item of the first user; and storing the ordered group and a record associating both the first user and the second user with the ordered group in the data store.

Abstract: The present invention relates to systems and methods for manipulating non-linearly connected transmedia content, in particular for creating, processing and managing non-linearly connected transmedia content. The disclosure includes content creation modes and version control systems for non-linearly connected transmedia content data, in which individual transmedia content data items are connected to either other via directional linking data.

Abstract: The invention relates to systems and methods for navigating, outputting and displaying non-linearly connected groups of transmedia content. Specifically, the invention involves retrieving, from a database, an ordered group of transmedia content data objects comprising a plurality of transmedia content data objects and linking data, whereby each element of the linking data defines a directional link from one of the transmedia content data objects to another of the transmedia content data objects; generating a two-dimensional graph structure representing the ordered group of transmedia content data objects whereby each graph node corresponds to a transmedia content data object and each graph edge corresponds to an element of the linking data; and outputting the graph structure on a display screen.

Abstract: The present disclosure relates to an apparatus, system and method for processing transmedia content data. More specifically, the disclosure provides for identifying and inserting one item of media content within another item of media content, e.g. inserting a video within a video, such that the first item of media content appears as part of the second item. The invention involves analysing a first visual media item to identify one or more spatial locations to insert the second visual media item within the image data of the first visual media item, detecting characteristics of the one or more identified spatial locations, transforming the second visual media item according to the detected characteristics and combining the first visual media item and second visual media item by inserting the transformed second visual media item into the first visual media item at the one or more identified spatial locations.

Abstract: Systems and techniques for generating a parametric eye model of one or more eyes are provided. The systems and techniques may include obtaining eye data from an eye model database. The eye data includes eyeball data and iris data corresponding to a plurality of eyes. The systems and techniques may further include generating an eyeball model using the eyeball data. Generating the eyeball model includes establishing correspondences among the plurality of eyes. The systems and techniques may further include generating an iris model using the iris data. Generating the iris model includes sampling one or more patches of one or more of the plurality of eyes using an iris control map and merging the one or more patches into a synthesized texture. The systems and techniques may further include generating the parametric eye model that includes the eyeball model and the iris model.