Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Footskate cleanup for motion capture editing
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Controlled animation of video sprites
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Planning biped locomotion using motion capture data and probabilistic roadmaps
ACM Transactions on Graphics (TOG)
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Snap-together motion: assembling run-time animations
ACM SIGGRAPH 2003 Papers
Precomputing avatar behavior from human motion data
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Dynamic response for motion capture animation
ACM SIGGRAPH 2005 Papers
Behavior planning for character animation
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Knowing when to put your foot down
I3D '06 Proceedings of the 2006 symposium on Interactive 3D graphics and games
State-annotated motion graphs for behavior control
State-annotated motion graphs for behavior control
Semi-automatic end-user tools for construction of virtual avatar behaviors
Proceedings of the 16th International Conference on 3D Web Technology
Planning interactive task for intelligent characters
Computer Animation and Virtual Worlds
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Motion graphs have gained popularity in recent years as a means for re-using motion capture data by connecting previously unrelated segments of a recorded library. Current techniques for controlling movement of a character via motion graphs have largely focused on path planning which is difficult due to the density of connections found on the graph. We introduce "state-annotated motion graphs," a novel technique which allows high-level control of character behavior by using a dual representation consisting of both a motion graph and a behavior state machine. This special motion graph is generated from labeled data and then bound to a finite state machine with similar labels. At run-time, character behavior is simply controlled by switching states. We show that it is possible to generate rich, controllable motion without the need for deep planning. We demonstrate that, when applied to an interactive fighting testbed, simple state-switching controllers may be coded intuitively to create various effects.