Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A hierarchical approach to interactive motion editing for human-like figures
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
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
Real Time Responsive Animation with Personality
IEEE Transactions on Visualization and Computer Graphics
Interpolation Synthesis of Articulated Figure Motion
IEEE Computer Graphics and Applications
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Rhythmic-motion synthesis based on motion-beat analysis
ACM SIGGRAPH 2003 Papers
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Enriching a motion collection by transplanting limbs
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion modeling for on-line locomotion synthesis
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Enriching a motion database by analogous combination of partial human motions
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
We present a novel methodology to quantitatively evaluate the synthesized motion generated by a motion-based animation technique. Our quantitative evaluation methodology provides a measure of how well each algorithm synthesizes motion based on their rotational and translational similarities to the ground truth in a motion database. To demonstrate the effectiveness of our methodology, we focus on techniques that combine different motions into a single spliced action where individual motions are performed simultaneously. We implement three splicing algorithms to perform a comparison study based on our quantitative evaluation methodology. The splicing algorithms considered are spatial body alignment, segmentation-based, and naïve DOF replacement. The spatial body alignment adapts the spliced motion according to this joint correlation and, consequently, performs best under our evaluation methodology.