Abstract: Techniques are disclosed for providing a learning-based clothing model that enables the simultaneous animation of multiple detailed garments in real-time. A simple conditional model learns and preserves key dynamic properties of cloth motions and folding details. Such a conditional model may be generated for each garment worn by a given character. Once generated, the conditional model may be used to determine complex body/cloth interactions in order to render the character and garment from frame-to-frame. The clothing model may be used for a variety of garments worn by male and female human characters (as well as non-human characters) while performing a varied set of motions typically used in video games (e.g., walking, running, jumping, turning, etc.).

Abstract: Various embodiments of the invention provide a control framework for robots such that a robot can use all joints simultaneously to track motion capture data and maintain balance. Embodiments of the invention provide a framework enabling complex reference movements to be automatically tracked, for example reference movements derived from a motion capture data system.

Abstract: The disclosure provides an approach for estimating a state-space controller from a set of video frames depicting a motion of an entity. The approach includes incrementally optimizing parameters of the state-space controller and changing a structure of the state-space controller based on expanding subsets of the set of video frames. In one embodiment, a controller-estimation application greedily selects, at every stage of the incremental optimization, structure and parameters of the controller which minimize an objective function. In another embodiment, the controller-estimation application re-optimizes, after the incremental optimization, all parameters of the state-space controller based on all of the video frames.

Abstract: Systems and method for modeling hair in real-time with user interactive controls are presented. One embodiment may take the form of a method of hair motion modeling including representing hair with hair guides, each hair guide comprising a plurality of hair points and reducing a dimensionality of the hair guides to achieve a reduced sub-space. Additionally, the method includes generating a data tensor for multiple factors related to the hair guides and decomposing the tensor to create a model characterizing the multiple factors in a multi-linear hair framework. The hair may be hair, such as human hair, animal fur, or clothing fibers.

Abstract: Various embodiments of the invention provide a control framework for robots such that a robot can use all joints simultaneously to track motion capture data and maintain balance. Embodiments of the invention provide a framework enabling complex reference movements to be automatically tracked, for example reference movements derived from a motion capture data system.

Abstract: Body-mounted cameras are used to accurately reconstruct the motion of a subject. Outward-looking cameras are attached to the limbs of the subject, and the joint angles and root pose that define the subject's configuration are estimated through a non-linear optimization, which can incorporate image matching error and temporal continuity of motion. Instrumentation of the environment is not required, allowing for motion capture over extended areas and in outdoor settings.

Abstract: Systems and method for modeling hair in real-time with user interactive controls are presented. One embodiment may take the form of a method of hair motion modeling including representing hair with hair guides, each hair guide comprising a plurality of hair points and reducing a dimensionality of the hair guides to achieve a reduced sub-space. Additionally, the method includes generating a data tensor for multiple factors related to the hair guides and decomposing the tensor to create a model characterizing the multiple factors in a multi-linear hair framework. The hair may be hair, such as human hair, animal fur, or clothing fibers.

Abstract: The disclosure provides an approach for estimating a state-space controller from a set of video frames depicting a motion of an entity. The approach includes incrementally optimizing parameters of the state-space controller and changing a structure of the state-space controller based on expanding subsets of the set of video frames. In one embodiment, a controller-estimation application greedily selects, at every stage of the incremental optimization, structure and parameters of the controller which minimize an objective function. In another embodiment, the controller-estimation application re-optimizes, after the incremental optimization, all parameters of the state-space controller based on all of the video frames.

Abstract: Various embodiments of the invention provide a control framework for robots such that a robot can use all joints simultaneously to track motion capture data and maintain balance. Embodiments of the invention provide a framework enabling complex reference movements to be automatically tracked, for example reference movements derived from a motion capture data system.

Abstract: Body-mounted cameras are used to accurately reconstruct the motion of a subject. Outward-looking cameras are attached to the limbs of the subject, and the joint angles and root pose that define the subject's configuration are estimated through a non-linear optimization, which can incorporate image matching error and temporal continuity of motion. Instrumentation of the environment is not required, allowing for motion capture over extended areas and in outdoor settings.

Abstract: A method for a computer system includes receiving global positional data associated with a set of markers from a plurality of markers associated with a surface of an object at one or more time instances, wherein global positional data associated with a first marker from the plurality of markers is absent from a first time instance, using local statistical methods to determine global positional data associated with the first marker at the first time instance in response to the global positional data associated with the set of markers at the one or more time instances, and determining a model of the object in response to the global positional data associated with the set of markers and the global positional data associated with the first marker.

Abstract: Techniques are disclosed for providing a learning-based clothing model that enables the simultaneous animation of multiple detailed garments in real-time. A simple conditional model learns and preserves key dynamic properties of cloth motions and folding details. Such a conditional model may be generated for each garment worn by a given character. Once generated, the conditional model may be used to determine complex body/cloth interactions in order to render the character and garment from frame-to-frame. The clothing model may be used for a variety of garments worn by male and female human characters (as well as non-human characters) while performing a varied set of motions typically used in video games (e.g., walking, running, jumping, turning, etc.).

Abstract: Various embodiments of the invention provide a control framework for robots such that a robot can use all joints simultaneously to track motion capture data and maintain balance. Embodiments of the invention provide a framework enabling complex reference movements to be automatically tracked, for example reference movements derived from a motion capture data system.

Abstract: Animation systems and methods are provided for modeling and animating objects, including breaking, tearing or cracking of the objects. In a preferred embodiment, the present invention includes setting initial conditions for an object, with the initial conditions including a first parameter defining a fracture condition. Preferably, the object is defined by at least one node. A second parameter corresponding to forces present at the node is determined. When the second parameter equals or exceeds the first parameter, a fracture is generated within the object with the fracture originating at the node.