Neural network learning and expert systems
Neural network learning and expert systems
Interactive spacetime control for animation
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Inverse kinematics positioning using nonlinear programming for highly articulated figures
ACM Transactions on Graphics (TOG)
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Efficient generation of motion transitions using spacetime constraints
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Motion editing with spacetime constraints
Proceedings of the 1997 symposium on Interactive 3D graphics
NeuroAnimator: fast neural network emulation and control of physics-based models
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Controlling physics in realistic character animation
Communications of the ACM
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Computer puppetry: An importance-based approach
ACM Transactions on Graphics (TOG)
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
A Rule-Based Interactive Behavioral Animation System for Humanoids
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
Constructive feedforward ART clustering networks. II
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Many researchers have taken the effort to describe the dynamics of the articulated body by the analytic method. They have obtained excellent results in various fields. However, for the articulated body moving with its voluntary will, it is difficult to generalize the motion pattern by analytical modeling, because the motion pattern is extremely subjective and unpredictable. The learning networks overcome the restriction of analytic modeling through the deductive learning method. The Uniform Posture Map (UPM) is proposed to synthesize a new motion between existing clip motions. It is organized through the quantization of various postures with an unsupervised learning algorithm; it places the output neurons with similar postures in adjacent positions. Using this property, an intermediate posture of applied two postures is generated; the generating posture is used as a key-frame to make an interpolating motion. The UPM needs fewer computational costs, in comparison with other motion transition algorithms. It provides a control parameter; an animator can not only control the motion simply by adjusting this parameter, but also produce animation interactively. The UPM prevents the generating of the invalid output neurons to present unreal postures in the learning phase; thus, it makes more realistic motion curves; finally it contributes to the making of more natural motions.