Abstract: Generally, the present disclosure is directed to systems and methods for measuring heart rate and respiratory rate using a camera such as, for example, a smartphone camera or other consumer-grade camera. Specifically, the present disclosure presents and validates two algorithms that make use of smartphone cameras (or the like) for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. As an example, HR can be measured by placing the finger of a subject over the rear-facing camera. As another example, RR can be measured via a video of the subject sitting still in front of the front-facing camera.

Abstract: A method for estimating two or more physiological signals from a subject includes steps of a) obtaining a video input in the form of a sequence of frames of image data depicting the face and optionally the chest of the subject; b) providing the video input to a multi-head neural network model trained from a set of facial video inputs from a multitude of other subjects (such video inputs optionally including the chest), wherein the model has at least two heads and is trained to predict at least two physiological signals from a video input; and c) generating with the model data representing an estimate of the two or more physiological signals of the subject. In one embodiment the physiological signals are heart rate and respiratory rate. In one embodiment the multi-head neural network model is implemented in a smartphone having a camera which is used to capture the video input.

Abstract: One exemplary process for animating hair includes receiving data representing a plurality of hairs and a plurality of objects in a timestep of a frame of animation. A first tree is populated to represent kinematic objects of the plurality of objects and a second tree is populated to represent dynamic objects of the plurality of objects based on the received data. A first elasticity preconditioner is created to represent internal elastic energy of the plurality of hairs based on the received data. Based on the first tree and the second tree, a first set of potential contacts is determined between two or more hairs of the plurality of hairs or between one or more hairs of the plurality of hairs and one or more objects of the plurality of objects. Positions of the plurality of hairs are determined based on the first set of potential contacts and the first elasticity preconditioner.

Abstract: In one general aspect, a method can include combining a partition polygon and a generated texture map to form a model of a scene for rendering in three dimensions in a virtual reality space. The generating of the texture map can include projecting a Layered Depth Image sample in a partition polygon to a point in a source camera window space, projecting the point back into the partition polygon as a surface element (surfel), projecting the surfel to a surfel footprint in a target camera window space, projecting from the target camera window space to the partition polygon, sub-pixel samples included in pixels covered by the surfel footprint, projecting the sub-pixel samples from the partition polygon and into the source camera window space, and applying a color weight to each sub-pixel sample based on the location of the sample in the source camera window space.

Abstract: One exemplary process for animating hair includes receiving data representing a plurality of hairs and a plurality of objects in a timestep of a frame of animation. A first tree is populated to represent kinematic objects of the plurality of objects and a second tree is populated to represent dynamic objects of the plurality of objects based on the received data. A first elasticity preconditioner is created to represent internal elastic energy of the plurality of hairs based on the received data. Based on the first tree and the second tree, a first set of potential contacts is determined between two or more hairs of the plurality of hairs or between one or more hairs of the plurality of hairs and one or more objects of the plurality of objects. Positions of the plurality of hairs are determined based on the first set of potential contacts and the first elasticity preconditioner.

Abstract: One exemplary process for animating hair includes receiving data representing a plurality of hairs and a plurality of objects in a timestep of a frame of animation. A first tree is populated to represent kinematic objects of the plurality of objects and a second tree is populated to represent dynamic objects of the plurality of objects based on the received data. A first elasticity preconditioner is created to represent internal elastic energy of the plurality of hairs based on the received data. Based on the first tree and the second tree, a first set of potential contacts is determined between two or more hairs of the plurality of hairs or between one or more hairs of the plurality of hairs and one or more objects of the plurality of objects. Positions of the plurality of hairs are determined based on the first set of potential contacts and the first elasticity preconditioner.

Abstract: One exemplary process for animating hair includes receiving data representing a plurality of hairs and a plurality of objects in a timestep of a frame of animation. A first tree is populated to represent kinematic objects of the plurality of objects and a second tree is populated to represent dynamic objects of the plurality of objects based on the received data. A first elasticity preconditioner is created to represent internal elastic energy of the plurality of hairs based on the received data. Based on the first tree and the second tree, a first set of potential contacts is determined between two or more hairs of the plurality of hairs or between one or more hairs of the plurality of hairs and one or more objects of the plurality of objects. Positions of the plurality of hairs are determined based on the first set of potential contacts and the first elasticity preconditioner.

Abstract: Simulating dynamics (e.g., physical effects of inertia, forces, wind) on strands (e.g., hair) during computer based animation requires quick and accurate approximations of mathematical curves. Each strand is initially represented as a B-spline curve. Line segments approximating the curve are created by using affine combinations based on the curve's control vertices. Dynamics simulation is performed on the line segment approximation. Once an approximated strand is simulated, it is converted back into a B-spine curve representation for downstream processes, such as rendering. The rendering process displays the simulated strand to the animator.

Abstract: Simulating dynamics (e.g., physical effects of inertia, forces, wind) on strands (e.g., hair) during computer based animation requires quick and accurate approximations of mathematical curves. Each strand is initially represented as a B-spline curve. Line segments approximating the curve are created by using affine combinations based on the curve's control vertices. Dynamics simulation is performed on the line segment approximation. Once an approximated strand is simulated, it is converted back into a B-spine curve representation for downstream processes, such as rendering. The rendering process displays the simulated strand to the animator.

Abstract: Computer graphics systems, devices and methods adapted to enable display and/or storage of human-perceptible images on a display device include an arrangement for generating a coarse level mesh representing a surface, from a finer level mesh surface representation. The arrangement includes an indicator value generator and a coarse level mesh generator. The indicator value generator, for respective ones of the points in the finer level mesh surface representation, evaluates an indicator function, the value indicating whether a subdivision-inverse filter methodology or a least-squares optimization methodology is to be used to determine a position for the corresponding point in the coarse level mesh representation.

Abstract: Computer graphics systems and methods are provided for generating a representation of a feature in a surface defined by a mesh representation, the mesh comprising at a selected level a plurality of points including at least one point connected to a plurality of neighboring points by respective edges, the feature being defined in connection with the vertex and at least one of the neighboring points and the edge interconnecting the vertex and the at least one of the neighboring points in the mesh representation. The feature generating arrangement comprises a weight vector generator module and a feature representation generator module.

Abstract: Animating strands (such as long hair), for movies, videos, etc. is accomplished using computer graphics by use of differential algebraic equations. Each strand is subject to simulation by defining its motion path, then evaluating dynamic forces acting on the strand. Collision detection with any objects is performed, and collision response forces are evaluated. Then for each frame a differential algebraic equations solver is invoked to simulate the strands.

Abstract: Computer graphics systems, devices and methods adapted to enable display and/or storage of human-perceptible images on a display, device include an arrangement for generating a coarse level mesh representing a surface, from a finer level mesh surface representation. The arrangement includes an indicator value generator and a coarse level mesh generator. The indicator value generator, for respective ones of the points in the finer level mesh surface representation, evaluates an indicator function, the value indicating whether a subdivision-inverse filter methodology or a least-squares optimization methodology is to be used to determine a position for the corresponding point in the coarse level mesh representation.

Abstract: Computer graphics systems and methods are provided for generating a representation of a feature in a surface defined by a mesh representation, the mesh comprising at a selected level a plurality of points including at least one point connected to a plurality of neighboring points by respective edges, the feature being defined in connection with the vertex and at least one of the neighboring points and the edge interconnection the vertex and the at least one of the neighboring points in the mesh representation. The feature generating arrangement comprises a weight vector generator module and a feature representation generator module.

Abstract: An arrangement is described for generating a representation of a feature in a surface defined by a mesh representation, the mesh comprising at a selected level a plurality of points including at least one point connected to a plurality of neighboring points by respective edges, the feature being defined in connection with the vertex and at least one of the neighboring points and the edge interconnecting the vertex and the at least one of the neighboring points in the mesh representation. The feature generating arrangement comprises a weight vector generator module and a feature representation generator module. The weight vector generator module is configured to generate at least one weight vector based on a parameterized subdivision rule defined at a plurality of levels, for which a value of at least one parameter differs at at least two levels in the mesh.

Abstract: Computer graphics systems and methods are provided for generating a representation of a feature in a surface defined by a mesh representation, the mesh comprising at a selected level a plurality of points including at least one point connected to a plurality of neighboring points by respective edges, the feature being defined in connection with the vertex and at least one of the neighboring points and the edge interconnection the vertex and the at least one of the neighboring points in the mesh representation. The feature generating arrangement comprises a weight vector generator module and a feature representation generator module.

Abstract: Computer graphics systems, devices and methods adapted to enable display and/or storage of human-perceptible images on a display, device include an arrangement for generating a coarse level mesh representing a surface, from a finer level mesh surface representation. The arrangement includes an indicator value generator and a coarse level mesh generator. The indicator value generator, for respective ones of the points in the finer level mesh surface representation, evaluates an indicator function, the value indicating whether a subdivision-inverse filter methodology or a least-squares optimization methodology is to be used to determine a position for the corresponding point in the coarse level mesh representation.

Abstract: An arrangement is disclosed for generating a coarse level mesh representing a surface, from a finer level mesh surface representation. The arrangement includes an indicator value generator and a coarse level mesh generator. The indicator value generator, for respective ones of the points in the finer level mesh surface representation, evaluates an indicator function, the value indicating whether a subdivision-inverse filter methodology or a least-squares optimization methodology is to be used to determine a position for the corresponding point in the coarse level mesh representation. The coarse level mesh generator determines, for each of the points that is to be provided in the coarse level mesh representation, a position in response to the position of the corresponding point in the finer level mesh representation, in accordance with the one of the subdivision-inverse filter methodology and least-squares optimization methodology indicated by the indicator value generated by the indicator value generator.

Abstract: An arrangement is disclosed for generating a coarse level mesh representing a surface, from a finer level mesh surface representation. The arrangement includes an indicator value generator and a coarse level mesh generator. The indicator value generator, for respective ones of the points in the finer level mesh surface representation, evaluates an indicator function, the value indicating whether a subdivision-inverse filter methodology or a least-squares optimization methodology is to be used to determine a position for the corresponding point in the coarse level mesh representation. The coarse level mesh generator determines, for each of the points that is to be provided in the coarse level mesh representation, a position in response to the position of the corresponding point in the finer level mesh representation, in accordance with the one of the subdivision-inverse filter methodology and least-squares optimization methodology indicated by the indicator value generated by the indicator value generator.

Abstract: An arrangement is described for generating a representation of a feature in a surface defined by a mesh representation, the mesh comprising at a selected level a plurality of points including at least one point connected to a plurality of neighboring points by respective edges, the feature being defined in connection with the vertex and at least one of the neighboring points and the edge interconnecting the vertex and the at least one of the neighboring points in the mesh representation. The feature generating arrangement comprises a weight vector generator module and a feature representation generator module. The weight vector generator module is configured to generate at least one weight vector based on a parameterized subdivision rule defined at a plurality of levels, for which a value of at least one parameter differs at at least two levels in the mesh.