Bayesian Reconstruction of 3D Human Motion from Single-Camera Video
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1 Bayesian Reconstruction of 3D Human Motion from Single-Camera Video Nicholas R. Howe Cornell University Michael E. Leventon MIT William T. Freeman Mitsubishi Electric Research Labs
2 Problem Background 2D video offers limited clues about actual 3D motion. Humans interpret 2D video easily. Goal: Reliable 3D reconstructions from standard single-camera input. November 30, 1999 NIPS
3 Research Progress Multi-camera trackers available: 1996: Gavrila & Davis; Kakadiaris & Metaxas Potential single-camera trackers: 1995: Goncalves et. al. 1997: Hunter, Kelly & Jain; Wachter & Nagel 1998: Morris & Rehg; Bregler & Malik Previous work: treated as measurement problem, not inference problem. November 30, 1999 NIPS
4 Challenges Single camera 3D ambiguity (underconstrained problem) Foreshortening Self-occlusion Unmarked video (no tags) Appearance changes Shadowing Clothing wrinkles November 30, 1999 NIPS
5 Overview of Approach Two stages to tracking, each challenging: 2D Tracking 3D Reconstruction November 30, 1999 NIPS
6 2D Tracking Predict 2D Pose, Model Refine 2D Pose 2D Pose + Model = Rendering Repeat for each frame. Compare with Image November 30, 1999 NIPS
7 2D Tracking Details Pose for first frame is given. Model derived from past frames. We use part map models. For each frame, begin at low resolution and refine. Rendering must account for selfocclusions. (need 3D feedback!) November 30, 1999 NIPS
8 Occlusion Must compute hidden pixels given pose. Only visible pixels matched with image. Model for hidden regions not updated. November 30, 1999 NIPS
9 2D Tracking Performance Simple example, no occlusion: Lines show tracked limb positions. November 30, 1999 NIPS
10 3D Reconstruction Motion divided into short movements, informally called snippets. (11 frames long) Assign probability to 3D snippets by analyzing knowledge base. Each snippet of 2D observations is matched to the most likely 3D motion. Resulting snippets are stitched together to reconstruct complete movement. November 30, 1999 NIPS
11 Learning Priors on Human Motion Collect known 3D motions, form snippets. Group similar movements, assemble matrix. SVD gives Gaussian probability cloud that generalizes to similar movements. November 30, 1999 NIPS
12 Posterior Probability Bayes Law gives probability of 3D snippet given the 2D observations: P(snip obs) = k P(obs snip) P(snip) Training database gives prior, P(snip). Assume normal distribution of tracking errors to get likelihood, P(obs snip). November 30, 1999 NIPS
13 November 30, 1999 NIPS Posterior Probability (cont.) Posterior is a mixture of multivariate Gaussian. Take negative log and minimize to find solution with MAP probability. Good solution can be found using off-theshelf numerics package. = = m j j x Y y j x T j x v s e k e k v s x P 1 ) (2 ) ( 1,, 2 2,, ),,, ( r r r r r r r r r α α σ π θ θ
14 Stitching Snippets overlap by 5 frames. Use weighted mean of overlapping snippets. November 30, 1999 NIPS
15 Sample Results: Test Data Test on known 3D data: Observation Reconstruction Comparison November 30, 1999 NIPS
16 Sample Results: Test Data Results on wave clip shown earlier: November 30, 1999 NIPS
17 Sample Results: Real Footage Can reconstruct even imperfect tracking: November 30, 1999 NIPS
18 Conclusion Treat 3D estimation from 2D video as an inference problem. Need to improve models Body appearance better rendering/tracking Motion better reconstruction Reliable single camera 3D reconstruction is within our grasp. November 30, 1999 NIPS
19 Final Video (Hand-tracked points, automatic reconstruction) November 30, 1999 NIPS
20 November 30, 1999 NIPS
21 2D Tracking Equation Must find pose parameters β that minimize matching energy: E( β ) = [ I ( p) I ( Project( p, β ))] ( Visible( b, p, β ) Image ) Body b Parts p Points( b) + E Model o( b) Accounts for self-occlusion Projection of model point into image. Additional constraints (joints, limb lengths, etc.) November 30, 1999 NIPS
22 2D Tracking Performance Simple example, no occlusion: November 30, 1999 NIPS
23 Sample Results: Test Data Test on known 3D data: Original Observations Reconstruction November 30, 1999 NIPS
24 Sample Results: Test Data Results on wave clip shown earlier: November 30, 1999 NIPS
25 Sample Results: Real Footage Can reconstruct even imperfect tracking: November 30, 1999 NIPS
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