Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Getting Humanoids to Move and Imitate
IEEE Intelligent Systems
Imitation in animals and artifacts
Imitation in animals and artifacts
Imitation in animals and artifacts
Imitation in animals and artifacts
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Dynamic response for motion capture animation
ACM SIGGRAPH 2005 Papers
Imitation as a first step to social learning in synthetic characters: a graph-based approach
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Imitation learning and response facilitation in embodied agents
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Grasp recognition for uncalibrated data gloves: A machine learning approach
Presence: Teleoperators and Virtual Environments
An animation system for imitation of object grasping in virtual reality
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Hi-index | 0.00 |
We present a novel method for virtual character animation that we call action capture. In this approach, virtual characters learn to imitate the actions of Virtual Reality (VR) users by tracking not only the users' movements but also their interactions with scene objects.Action capture builds on conventional motion capture but differs from it in that higher-level action representations are transferred rather than low-level motion data. As an advantage, the learned actions can often be naturally applied to varying situations, thus avoiding retargetting problems of motion capture. The idea of action capture is inspired by human imitation learning; related methods have been investigated for a longer time in robotics. The paper reviews the relevant literature in these areas before framing the concept of action capture in the context of VR-based character animation. We also present an example in which the actions of a VR user are transferred to a virtual worked.