Name
Abstract | ||
|---|---|---|
We show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). Based on an extensive corpus of motion capture data, with physical and perceptual ground truth, we analyze the inference of subtle biologically-inspired attributes from cyclic gait data. It is shown that inference is also possible with partial observations of the body, and with motions as short as a single gait cycle. Learning models from small amounts of noisy video pose data is, however, prone to over-fitting. To mitigate this we formulate learning in terms of domain adaptation, for which mocap data is uses to regularize models for inference from video-based data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15558-1_18 | ECCV (3) |
Keywords | Field | DocType |
conventional euclidean joint error,mocap data,extensive corpus,single gait cycle,physical attribute,video-based data,cyclic gait data,man-machine communication,domain adaptation,motion capture data,human attribute,3d pose estimation,ground truth | Computer vision,Motion capture,Inference,Computer science,Biological motion,Transfer of learning,3D pose estimation,Ground truth,Artificial intelligence,Articulated body pose estimation,Perception,Machine learning | Conference |
Volume | ISSN | ISBN |
6313 | 0302-9743 | 3-642-15557-X |
Citations | PageRank | References |
17 | 0.89 | 37 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonid Sigal | 1 | 2163 | 124.33 |
| David J. Fleet | 2 | 5236 | 550.74 |
| Niko Troje | 3 | 78 | 8.75 |
| Micha Livne | 4 | 33 | 2.28 |