Learning to Reconstruct 3D Human Pose and Motion from Silhouettes
author:
Ankur Agarwal,
Institut National de Recherche en Informatique et en Automatique
Description
We will describe our ongoing work on learning-based methods for recovering 3D human body pose and motion from single images and from monocular image sequences. The methods work directly with raw image observations and require neither an explicit 3D body model nor a prior labelling of body parts in the image. Instead, they recover the body pose or motion by direct nonlinear regression against shape descriptors extracted automatically from image silhouettes or contours.
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| Slides | |
| 0:01 | Learning to Reconstruct 3D Human Pose and Motion from Silhouettes |
| 0:17 | Goal |
| 1:00 | 2 Broad Classes of Approaches |
| 2:54 | “Model Free” Learning – based Approach |
| 4:02 | The Basic Idea |
| 4:56 | Silhouette Descriptors |
| 5:03 | Why Use Silhouettes ? |
| 6:19 | Ambiguities |
| 7:23 | Shape Context Histograms |
| 10:18 | Shape Context Histograms Encode Locality |
| 11:22 | Nonlinear Regression |
| 11:31 | Regression Model |
| 12:59 | Regularized Least Squares |
| 14:00 | Relevance Vector Machine … a brief introduction |
| 16:19 | Contd. |
| 17:25 | Pose from Static Images |
| 17:30 | Training & Test Data |
| 18:59 | Methods Tested |
| 19:49 | Synthetic Spiral Walk Test Sequence |
| 20:52 | Spiral Walk Test Sequence |
| 21:58 | Some statistics .. |
| 22:56 | Glitches |
| 23:45 | TITLE |
| 24:06 | Real Image example |
| 24:19 | Understanding the Problem |
| 25:00 | Pose from Video Sequences |
| 25:03 | Tracking Framework |
| 25:35 | Joint Regression equations |
| 26:32 | Results with Joint Regression |
| 26:45 | Spiral Walk Test Sequence |
| 27:25 | Real Images Test Sequence |
| 27:41 | Conclusion |
| 30:43 | Real Images Test Sequence |
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