Abstract: The disclosure provides an approach for learning to schedule control fragments for physics-based virtual character simulations and physical robot control. Given precomputed tracking controllers, a simulation application segments the controllers into control fragments and learns a scheduler that selects control fragments at runtime to accomplish a task. In one embodiment, each scheduler may be modeled with a Q-network that maps a high-level representation of the state of the simulation to a control fragment for execution. In such a case, the deep Q-learning algorithm applied to learn the Q-network schedulers may be adapted to use a reward function that prefers the original controller sequence and an exploration strategy that gives more chance to in-sequence control fragments than to out-of-sequence control fragments.

Abstract: The disclosure provides an approach for learning to schedule control fragments for physics-based virtual character simulations and physical robot control. Given precomputed tracking controllers, a simulation application segments the controllers into control fragments and learns a scheduler that selects control fragments at runtime to accomplish a task. In one embodiment, each scheduler may be modeled with a Q-network that maps a high-level representation of the state of the simulation to a control fragment for execution. In such a case, the deep Q-learning algorithm applied to learn the Q-network schedulers may be adapted to use a reward function that prefers the original controller sequence and an exploration strategy that gives more chance to in-sequence control fragments than to out-of-sequence control fragments.

Abstract: Described herein are methods, systems, apparatuses and products for utilizing motion fields to predict evolution in dynamic scenes. One aspect provides for accessing active object position data including positioning information of a plurality of individual active objects; extracting a plurality of individual active object motions from the active object position data; constructing a motion field using the plurality of individual active object motions; and using the motion field to predict one or more points of convergence at one or more spatial locations that active objects are proceeding towards at a future point in time. Other embodiments are disclosed.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: Embodiments of the invention provide an approach for reproducing a human action with a robot. The approach includes receiving data representing motions and contact forces of the human as the human performs the action. The approach further includes approximating, based on the motions and contact forces data, the center of mass (CoM) trajectory of the human in performing the action. Finally, the approach includes generating a planned robot action for emulating the designated action by solving an inverse kinematics problem having the approximated human CoM trajectory as a hard constraint and the motion capture data as a soft constraint.

Abstract: Systems, methods and products for animating non-humanoid characters with human motion are described. One aspect includes selecting key poses included in initial motion data at a computing system; obtaining non-humanoid character key poses which provide a one to one correspondence to selected key poses in said initial motion data; and statically mapping poses of said initial motion data to non-humanoid character poses using a model built based on said one to one correspondence from said key poses of said initial motion data to said non-humanoid character key poses. Other embodiments are described.

Abstract: Techniques are disclosed for augmenting hand-drawn animation of human characters with three-dimensional (3D) physical effects to create secondary motion. Secondary motion, or the motion of objects in response to that of the primary character, is widely used to amplify the audience's response to the character's motion and to provide a connection to the environment. These 3D effects are largely passive and tend to be time consuming to animate by hand, yet most are very effectively simulated in current animation software. The techniques enable hand-drawn characters to interact with simulated objects such as cloth and clothing, balls and particles, and fluids. The driving points or volumes for the secondary motion are tracked in two dimensions, reconstructed into three dimensions, and used to drive and collide with the simulated objects.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: Systems, methods and products for animating non-humanoid characters with human motion are described. One aspect includes selecting key poses included in initial motion data at a computing system; obtaining non-humanoid character key poses which provide a one to one correspondence to selected key poses in said initial motion data; and statically mapping poses of said initial motion data to non-humanoid character poses using a model built based on said one to one correspondence from said key poses of said initial motion data to said non-humanoid character key poses. Other embodiments are described.

Abstract: Systems, methods and products for animating non-humanoid characters with human motion are described. One aspect includes selecting key poses included in initial motion data at a computing system; obtaining non-humanoid character key poses which provide a one to one correspondence to selected key poses in said initial motion data; and statically mapping poses of said initial motion data to non-humanoid character poses using a model built based on said one to one correspondence from said key poses of said initial motion data to said non-humanoid character key poses. Other embodiments are described.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: Embodiments of the invention provide an approach for reproducing a human action with a robot. The approach includes receiving data representing motions and contact forces of the human as the human performs the action. The approach further includes approximating, based on the motions and contact forces data, the center of mass (CoM) trajectory of the human in performing the action. Finally, the approach includes generating a planned robot action for emulating the designated action by solving an inverse kinematics problem having the approximated human CoM trajectory as a hard constraint and the motion capture data as a soft constraint.

Abstract: Systems, methods and products for animating non-humanoid characters with human motion are described. One aspect includes selecting key poses included in initial motion data at a computing system; obtaining non-humanoid character key poses which provide a one to one correspondence to selected key poses in said initial motion data; and statically mapping poses of said initial motion data to non-humanoid character poses using a model built based on said one to one correspondence from said key poses of said initial motion data to said non-humanoid character key poses. Other embodiments are described.

Abstract: Described herein are methods, systems, apparatuses and products for utilizing motion fields to predict evolution in dynamic scenes. One aspect provides for accessing active object position data including positioning information of a plurality of individual active objects; extracting a plurality of individual active object motions from the active object position data; constructing a motion field using the plurality of individual active object motions; and using the motion field to predict one or more points of convergence at one or more spatial locations that active objects are proceeding towards at a future point in time. Other embodiments are disclosed.