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
Motion capture assisted animation: texturing and synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Movement Phase in Signs and Co-Speech Gestures, and Their Transcriptions by Human Coders
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Personalised Real-Time Idle Motion Synthesis
PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
A framework of combining Markov model with association rules for predicting web page accesses
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Synthesis and evaluation of linear motion transitions
ACM Transactions on Graphics (TOG)
Gesture modeling and animation based on a probabilistic re-creation of speaker style
ACM Transactions on Graphics (TOG)
SmartBody: behavior realization for embodied conversational agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Learning a model of speaker head nods using gesture corpora
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Postural expressions of action tendencies
Proceedings of the 2nd international workshop on Social signal processing
Gesture synthesis adapted to speech emphasis
Speech Communication
Hi-index | 0.00 |
Effective speakers engage their whole body when they gesture. It is difficult, however, to create such full body motion in animated agents while still supporting a large and flexible gesture set. This paper presents a hybrid system that combines motion capture data with a procedural animation system for arm gestures. Procedural approaches are well suited to supporting a large and easily modified set of gestures, but are less adept at producing subtle, full body movement. Our system aligns small motion capture samples of lower body movement, and procedurally generated spine rotation, with gesture strokes to create convincing full-body movement. A combined prediction model based on a Markov model and association rules is used to select these clips. Given basic information on the stroke, the system is fully automatic. A user study compares three cases: the model turned off, and two variants of our algorithm. Both versions of the model were shown to be preferable to no model and guidance is given on which variant is preferable.