Name
Abstract | ||
|---|---|---|
Processing motion capture data from optical markers for use in computer animations presents numerous technical challenges. Artifacts caused by noise, marker swaps, and marker occlusions often require manual intervention of a professionally trained marker tracking artist that spends large amounts of time and effort fixing these issues. Existing automatic solutions that attempt to fix marker data lack robustness due to either failing to properly detect and fix marker paths, or generating solutions that are challenging to integrate within current animation pipelines. In this paper, we present a method that robustly identifies invalid marker paths, removes the associated segments and generates new kinematically correct paths. We start by comparing the kinematic solutions generated by commercial software against the one generated by the state-of-the-art methods, using this information to determine which animation keyframes are invalid. Subsequently, we regenerate marker paths from the neural network based method [Holden 2018] and use a sophisticated marker filling algorithm to combine them with the original marker paths at sections where we detect the original data to be invalid. Our method outperforms alternatives by generating solutions that are both closer to the ground truth and more robust, allowing for manual intervention if required.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3359566.3360060 | MIG '19: Motion, Interaction and Games
Newcastle upon Tyne
United Kingdom
October, 2019 |
Field | DocType | ISBN |
Computer vision,Kinematics,Computer science,Artificial intelligence,Trajectory | Conference | 978-1-4503-6994-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maksym Perepichka | 1 | 0 | 1.01 |
| Daniel Holden | 2 | 0 | 0.34 |
| Sudhir P. Mudur | 3 | 201 | 45.52 |
| Tiberiu Popa | 4 | 473 | 32.74 |