Name
Abstract | ||
|---|---|---|
We present a technique for learning a manifold of human motion data using Convolutional Autoencoders. Our approach is capable of learning a manifold on the complete CMU database of human motion. This manifold can be treated as a prior probability distribution over human motion data, which has many applications in animation research, including projecting invalid or corrupt motion onto the manifold for removing error, computing similarity between motions using geodesic distance along the manifold, and interpolation of motion along the manifold for avoiding blending artefacts. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2820903.2820918 | SIGGRAPH Asia Technical Briefs |
Field | DocType | Citations |
Computer vision,Pattern recognition,Computer science,Convolutional neural network,Interpolation,Character animation,Manifold alignment,Animation,Artificial intelligence,Nonlinear dimensionality reduction,Geodesic,Manifold | Conference | 30 |
PageRank | References | Authors |
1.19 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Holden | 1 | 176 | 9.75 |
| Jun Saito | 2 | 99 | 5.18 |
| Taku Komura | 3 | 2343 | 142.42 |
| Thomas Joyce | 4 | 30 | 1.87 |