Abstract: Systems and methods for performing remote markerless motion capture to drive 3D animations in real time in accordance with embodiments of the invention are described. One embodiment of the invention includes an optical device connected to a data acquisition device, where the combination of the optical device and the data acquisition device is configured to perform markerless motion capture, and a server system configured to communicate with the data acquisition device via the Internet. In addition, the server system is configured to receive motion capture data from the data acquisition device, and the server system is configured to generate motion data to animate a 3D character model based upon the received motion capture data.

Abstract: Systems and methods are described for animating 3D characters using synthetic motion data generated by motion models in response to a high level description of a desired sequence of motion provided by an animator. In a number of embodiments, the synthetic motion data is streamed to a user device that includes a rendering engine and the user device renders an animation of a 3D character using the streamed synthetic motion data. In several embodiments, an animator can upload a custom model of a 3D character or a custom 3D character is generated by the server system in response to a high level description of a desired 3D character provided by the user and the synthetic motion data generated by the generative model is retargeted to animate the custom 3D character.

Abstract: A markerless motion capture system is provided for measurements accurate enough for biomechanical, clinical, sport, entertainment, animation, game and movie, design, ergonomics, surveillance applications. The system has multiple cameras distributed around a viewing volume. The cameras allow for the creation of three-dimensional mesh representations of an object dynamically moving within the viewing volume. A model of the object that incorporates specific morphological and kinematic model information (including soft joint constraints) is then matched to the captured three-dimensional mesh representations. The matching routine aims to embed the model into each of the three-dimensional representations using (i) iterative closest point or simulated annealing algorithms and (ii) using soft joint constraints. This unique combination of routines offers a simple, time-efficient, accurate and thus more meaningful assessment of movements.

Abstract: Systems and methods are described for performing spatial and temporal compression of deformable mesh based representations of 3D character motion allowing the visualization of high-resolution 3D character animations in real time. In a number of embodiments, the deformable mesh based representation of the 3D character motion is used to automatically generate an interconnected graph based representation of the same 3D character motion. The interconnected graph based representation can include an interconnected graph that is used to drive mesh clusters during the rendering of a 3D character animation. The interconnected graph based representation provides spatial compression of the deformable mesh based representation, and further compression can be achieved by applying temporal compression processes to the time-varying behavior of the mesh clusters.

Abstract: Systems and methods for automatically generating animation-ready 3D character models based upon model parameter and clothing selections are described. One embodiment of the invention includes an application server configured to receive the user defined model parameters and the clothing selection via a user interface.

Abstract: Systems and methods are described for animating 3D characters using synthetic motion data generated by generative models in response to a high level description of a desired sequence of motion provided by an animator. In a number of embodiments, an animation system is accessible via a server system that utilizes the ability of generative models to generate synthetic motion data across a continuum to enable multiple animators to effectively reuse the same set of previously recorded motion capture data to produce a wide variety of desired animation sequences. In several embodiments, an animator can upload a custom model of a 3D character and the synthetic motion data generated by the generative model is retargeted to animate the custom 3D character.

Abstract: An automated method for the generation of (i) human models comprehensive of shape and joint centers information and/or (ii) subject specific models from multiple video streams is provided. To achieve these objectives, a kinematic model is learnt space from a training data set. The training data set includes kinematic models associated with corresponding morphological models. A shape model is identified as well as one or more poses of the subject. The learnt kinematic model space and the identified shape model are combined to generate a full body model of the subject starting from as few as one-static pose. Further, to generate a full body model of an arbitrary human subject, the learnt kinematic model space and the identified shape model are combined using a parameter set. The invention is applicable for fully automatic markerless motion capture and generation of complete human models.

Abstract: A markerless motion capture system is provided for measurements accurate enough for biomechanical, clinical, sport, entertainment, animation, game and movie, design, ergonomics, surveillance applications. The system has multiple cameras distributed around a viewing volume. The cameras allow for the creation of three-dimensional mesh representations of an object dynamically moving within the viewing volume. A model of the object that incorporates specific morphological and kinematic model information (including soft joint constraints) is then matched to the captured three-dimensional mesh representations. The matching routine aims to embed the model into each of the three-dimensional representations using (i) iterative closest point or simulated annealing algorithms and (ii) using soft joint constraints. This unique combination of routines offers a simple, time-efficient, accurate and thus more meaningful assessment of movements.