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Pattern Recognition and Machine Learning in Computer Vision Workshop
Pascal

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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