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Pose Machine

Pose Machines is a method for estimating articulated human pose from images using convolutional neural networks. It uses a multi-stage approach where each stage predicts confidence maps for body parts using features from the previous stage. This allows the model to incorporate strong contextual cues between related body joints across stages. Key aspects of the approach include using intermediate supervision to address vanishing gradients, large receptive fields to capture context, and hierarchical prediction of parts to leverage top-down cues. Evaluation on public datasets shows it achieves state-of-the-art performance for articulated human pose estimation from single images.

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Pose Machines Estimating Articulated Pose from Images Robotics Institute Carnegie Mellon University Convolutional Pose Machines. Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. Pose Machines: Articulated Pose Estimation via Inference Machines. Varun Ramakrishna, Daniel Munoz, Martial Hebert, J.A. Bagnell, Yaser Sheikh. In ECCV 2014 (Oral presentation). 2016/8/11 1
Goal: Articulated Pose Estimation 2016/8/11 2
Goal: Articulated Pose Estimation https://www.youtube.com/watch?v=Oi_ycvFHd64&index=6&list=PLNh5A7HtLRcpsMfvyG0DED-Dr4zW5Lpcg 2016/8/11 3
Goal: Articulated Pose Estimation https://www.youtube.com/watch?v=MsZkLK0Wcmk&list=PLNh5A7HtLRcpsMfvyG0DED-Dr4zW5Lpcg&index=1 2016/8/11 4
Which part corresponds to a body part? • Local evidence is weak • Part context is a strong cue • Top-down cues are helpful2016/8/11 5
Using Local Image Evidence Multi-ClassClassificationof Patches g1 Image Features 1xz ImageLocation z Input Image handsfeet Requires a high-capacity supervised predictor capable of handling multi-modal data2016/8/11 6
Using Local Image Evidence A ClassicalSlidingWindowDetectionPipeline Image Feature Extraction Classification 2016/8/11 7
Local Image Evidence is Weak • Certain parts are easier to detect than others head neck l.shoulder l.elbow l.wrist 2016/8/11 8
Part Context is a Strong Cue Part detection confidences provide spatial context cues L-ShoulderL-ElbowImage Neck 2016/8/11 9
Tree Structures vs Loopy Graphs Tree Structures • Fast and exact inference • Double counting Loopy Graphs • Rich context • Approximate inference 2015/9/11 10
Designing Context Representations Context features encode responses of aprevious prediction stage Offset Features Patch Features Image 2016/8/11 L b11
Context Features g2 g3 Stage II Stage III Confidence Maps Confidence Maps g1 Context Features Stage I Confidence Maps Stage I Confidence Image Features Head Neck L-Shoulder L-Elbow L-Wrist 2016/8/11 L b12
g2g1 Context Features g3 Image Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence Maps Stage II Confidence Head Neck L-Shoulder L-Elbow L-Wrist 2016/8/11 L b13
g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence Maps Image Features Stage III Confidence Head Neck L-Shoulder L-Elbow L-Wrist 2016/8/11 L b14
Level 1 parts Level 2 poselet Level 3 full body [Bourdev et al., CVPR2009][Sun et al., CVPR2012] [Duan et al., BMVC 2012][Singh et al., ECCV2012] [Pishchulin et al., CVPR2013] etc. Top Down Cues are Helpful Larger Composite Parts can be Easier to detect 2016/8/11 15
2 gT 1 gT Stage t = (T = 3) Context Features Context Context Features Image Features Features Context Features Context Features Context Features Image Features Image Features Image Features 2g1 L g1 Stage t = 1 1 g1 Level 1 Level 2 LevelL Image Features Image Features Image Features L g2 2 g2 1 g Stage t = 2 Incorporating Hierarchical Cues • Each level of the hierarchy uses a separate predictor • Context features are computed on the outputs of the previous stage • Spatial context information is passed across layers via context features L gT 2016/8/11 16
1g2 1g1 Level 1 1 gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Stage t = 1 Stage t = 2 Level I Confidence Maps L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. StageIStageIIStageIII 2016/8/11 L b17
Stage t = 2 Level 2 Confidence Maps StageIStageIIStageIII Head+Sho L.Arm R.Arm Torso L.Leg Bkgd.R.Leg 1g2 1g1 Level 1 1 gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Stage t = 1 L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL 2016/8/11 L b18
Stage t = 2 Level 3 Confidence Maps Torso Bkgd. Stage I Stage II Stage III 1g2 1g1 Level 1 1 gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Stage t = 1 L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL 2016/8/11 L b19
1g2 1g1 Level 1 1gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Stage t = 1 Stage t = 2 L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL Fully Connected Model 2016/8/11 L b20
Pose Machines Sequential Prediction with Spatial Context Training reduces to training multiple supervised classifiers g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence MapsImage Features Image Features Image Features No structured loss function No specialized solvers No handcrafted spatial model Spatial model is learned implicitly by the classifiers in a data-driven fashion 2016/8/11 21
Learning Feature Representations • Convolutional Architectures for Feature Embedding 2016/8/11 22
Learning Context Representations • Large Receptive Fields as a Design Criterion 2016/8/11 23
Learning Context Representations • Large Receptive Fields Improve Pose Estimation 2016/8/11 24
Convolutional Pose Machines • Designing a Convolutional Architecture 2016/8/11 25
Learning • Joint Training with Intermediate Supervision 𝑓𝑡 = − 2 2 Loss: Euclidean distance groundtruth prediction Network without Intermediate Supervision leads vanishing gradients 2016/8/11 26
Input Stage 1 Layer 1 Layer 3 Layer 6 4 1 10 3 10 Epoch 10 2 1 10 0 10 Output Layer 18 Stage 2 Layer 7 Layer 9 Layer 12 Layer 13 Stage 3 Layer 15 4 2 10 3 10 Epoch 10 2 1 10 0 10 4 3 10 3 10 Epoch 10 2 1 10 0 10 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 Supervision Supervision Histograms of Gradient Magnitude During Training Supervision Learning Intermediate Supervision Addresses Vanishing Gradients Gradient Magnitude 10 Gradient (× 10−3 ) With Intermediate Supervision Without Intermediate Supervision 0 10 1 10 2 10 3 10 4 Input Image h w 3 5⇥5 C 5⇥5 C 2⇥ 5⇥5 9⇥9 1⇥1 1⇥1 P C C C C 9⇥9 C 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 2⇥ P 5⇥5 C 5⇥5 C 5⇥5 C 2⇥ P 2⇥ P Input Image h w 3 h0 w0 P1+1 P1+1 9⇥9 C Loss 1 f 2 Loss 1f 1 x1 1 x1 2 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 5⇥5 2⇥ 5⇥5 2⇥ 5⇥5 C P C P C Input Image h w 3 h0 w0 P1+1 Loss 1f 3 x1 2 h0 w0 Stage 3, level 1Stage 2, level 1Stage 1, level 1 2016/8/11 27
Input Layer 1 Output Layer 18 10 0 10 1 10 2 10 3 10 4 Epoch1 Stage 1 Layer 3 Layer 6 Layer 7 Stage 2 Layer 9 Layer 12 Layer 13 Stage 3 Layer 15 10 0 10 1 10 2 10 3 10 4 Epoch2 −0.5 0.0 0.5 10 0 10 1 10 2 10 3 10 4 Epoch3 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 Histograms of Gradient Magnitude During Training Supervision Supervision Supervision Input Image h w 3 5⇥5 C 5⇥5 C 2⇥ 5⇥5 9⇥9 1⇥1 1⇥1 P C C C C 9⇥9 C 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 2⇥ P 5⇥5 C 5⇥5 C 5⇥5 C 2⇥ P 2⇥ P Input Image h w 3 h0 w0 P1+1 P1+1 9⇥9 C Loss 1 f 2 Loss 1f 1 x1 1 x1 2 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 5⇥5 2⇥ 5⇥5 2⇥ 5⇥5 C P C P C Input Image h w 3 h0 w0 P1+1 Loss 1f 3 x1 2 h0 w0 Gradient (× 10−3 ) With Intermediate Supervision Without Intermediate Supervision Stage 3, level 1Stage 2, level 1Stage 1, level 1 Learning Intermediate Supervision Addresses Vanishing Gradients 2016/8/11 28
0 0 Detectionrate% (i) With Intermediate Supervision (IS) (ii) Stagewise (iii) IS + Stagewise Pretrain (iv) Without Intermediate Supervision 0.05 0.1 0.15 0.2 Normalized distance 100 90 80 70 60 50 40 30 20 10 PCK total, LSP OC Learning Comparison of Learning Methods 2016/8/11 29
Qualitative Results 2016/8/11 L b30
Evaluation Qualitative Examples on LEEDS (Person-centric) 2016/8/11 L b31
Evaluation Qualitative Examples on MPI (Person-centric) 2016/8/11 L b32
Resolving Symmetric Confusions LeftRight t = 1 t = 2 t = 3 Wrists 2016/8/11 L b33
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 34
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Stage II Stage I Stage III Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Context from the confidence map of head is removed Ablative Spatial Analysis 2016/8/11 35
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 36
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 37
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) StageIIStageIStageIII Predicted Pose Ablative Spatial Analysis 2016/8/11 38
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 39
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 40
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 41
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 42
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 43
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 44
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 45
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 46
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 47
Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 48
0 0.05 0.1 0.15 Normalized distance 0.2 0 0 100 90 80 70 60 50 40 30 20 10 Detectionrate% Ours 3−Stage 2−Level Tompson et al., CVPR’15 Tompson et al., NIPS’14 Chen&Yullie, NIPS’14 Toshev et al., CVPR’14 Sapp et al., CVPR’13 Evaluation PCK Performance Comparison on FLIC dataset PCK wrist, FLIC 0.05 0.1 0.15 Normalized distance 0.2 PCK elbow, FLIC 2016/8/11 49
0 0.05 0.1 0.15 Normalized distance Ours 3−Stage 2−Level 0.2 0 0 100 90 80 70 60 50 40 30 20 10 PCK total, LSP PC Detectionrate% Tompson et al., NIPS’14 Pishchulin et al., ICCV’13 Chen&Yuille, NIPS’14 Wang et al., CVPR’13 0.05 0.1 0.15 0.2 0 Normalized distance 0.05 0.1 0.15 0.2 0 Normalized distance PCK wrist&elbow, LSP PC 0.05 0.1 0.15 0.2 0 Normalized distance PCK knee, LSP PC 0.05 0.1 0.15 0.2 PCK ankle, LSP PC Normalized distance PCK hip, LSP PC Evaluation PCK Performance Comparison on LEEDS dataset (Person-centric) 2016/8/11 50

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Pose Machine

  • 1.
    Pose Machines Estimating ArticulatedPose from Images Robotics Institute Carnegie Mellon University Convolutional Pose Machines. Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. Pose Machines: Articulated Pose Estimation via Inference Machines. Varun Ramakrishna, Daniel Munoz, Martial Hebert, J.A. Bagnell, Yaser Sheikh. In ECCV 2014 (Oral presentation). 2016/8/11 1
  • 2.
    Goal: Articulated PoseEstimation 2016/8/11 2
  • 3.
    Goal: Articulated PoseEstimation https://www.youtube.com/watch?v=Oi_ycvFHd64&index=6&list=PLNh5A7HtLRcpsMfvyG0DED-Dr4zW5Lpcg 2016/8/11 3
  • 4.
    Goal: Articulated PoseEstimation https://www.youtube.com/watch?v=MsZkLK0Wcmk&list=PLNh5A7HtLRcpsMfvyG0DED-Dr4zW5Lpcg&index=1 2016/8/11 4
  • 5.
    Which part correspondsto a body part? • Local evidence is weak • Part context is a strong cue • Top-down cues are helpful2016/8/11 5
  • 6.
    Using Local ImageEvidence Multi-ClassClassificationof Patches g1 Image Features 1xz ImageLocation z Input Image handsfeet Requires a high-capacity supervised predictor capable of handling multi-modal data2016/8/11 6
  • 7.
    Using Local ImageEvidence A ClassicalSlidingWindowDetectionPipeline Image Feature Extraction Classification 2016/8/11 7
  • 8.
    Local Image Evidenceis Weak • Certain parts are easier to detect than others head neck l.shoulder l.elbow l.wrist 2016/8/11 8
  • 9.
    Part Context isa Strong Cue Part detection confidences provide spatial context cues L-ShoulderL-ElbowImage Neck 2016/8/11 9
  • 10.
    Tree Structures vsLoopy Graphs Tree Structures • Fast and exact inference • Double counting Loopy Graphs • Rich context • Approximate inference 2015/9/11 10
  • 11.
    Designing Context Representations Contextfeatures encode responses of aprevious prediction stage Offset Features Patch Features Image 2016/8/11 L b11
  • 12.
    Context Features g2 g3 Stage IIStage III Confidence Maps Confidence Maps g1 Context Features Stage I Confidence Maps Stage I Confidence Image Features Head Neck L-Shoulder L-Elbow L-Wrist 2016/8/11 L b12
  • 13.
    g2g1 Context Features g3 Image Features Context Features Stage I Confidence Maps StageII Confidence Maps Stage III Confidence Maps Stage II Confidence Head Neck L-Shoulder L-Elbow L-Wrist 2016/8/11 L b13
  • 14.
    g2g1 g3 Context Features Context Features Stage I ConfidenceMaps Stage II Confidence Maps Stage III Confidence Maps Image Features Stage III Confidence Head Neck L-Shoulder L-Elbow L-Wrist 2016/8/11 L b14
  • 15.
    Level 1 partsLevel 2 poselet Level 3 full body [Bourdev et al., CVPR2009][Sun et al., CVPR2012] [Duan et al., BMVC 2012][Singh et al., ECCV2012] [Pishchulin et al., CVPR2013] etc. Top Down Cues are Helpful Larger Composite Parts can be Easier to detect 2016/8/11 15
  • 16.
    2 gT 1 gT Stage t =(T = 3) Context Features Context Context Features Image Features Features Context Features Context Features Context Features Image Features Image Features Image Features 2g1 L g1 Stage t = 1 1 g1 Level 1 Level 2 LevelL Image Features Image Features Image Features L g2 2 g2 1 g Stage t = 2 Incorporating Hierarchical Cues • Each level of the hierarchy uses a separate predictor • Context features are computed on the outputs of the previous stage • Spatial context information is passed across layers via context features L gT 2016/8/11 16
  • 17.
    1g2 1g1 Level 1 1 gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Staget = 1 Stage t = 2 Level I Confidence Maps L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL Head Neck L.Sho. L.Elb. R.Hip R.Knee R.Ank. Bkgd. StageIStageIIStageIII 2016/8/11 L b17
  • 18.
    Stage t =2 Level 2 Confidence Maps StageIStageIIStageIII Head+Sho L.Arm R.Arm Torso L.Leg Bkgd.R.Leg 1g2 1g1 Level 1 1 gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Stage t = 1 L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL 2016/8/11 L b18
  • 19.
    Stage t =2 Level 3 Confidence Maps Torso Bkgd. Stage I Stage II Stage III 1g2 1g1 Level 1 1 gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Stage t = 1 L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL 2016/8/11 L b19
  • 20.
    1g2 1g1 Level 1 1gT Image Features Image Features Image Features Context Features Context Features Level 2 2g1 L g1 L g2 2g2 Staget = 1 Stage t = 2 L gT 2gT Stage t = (T = 3) Context Features Context Features Context Features Context Features LevelL Fully Connected Model 2016/8/11 L b20
  • 21.
    Pose Machines Sequential Predictionwith Spatial Context Training reduces to training multiple supervised classifiers g2g1 g3 Context Features Context Features Stage I Confidence Maps Stage II Confidence Maps Stage III Confidence MapsImage Features Image Features Image Features No structured loss function No specialized solvers No handcrafted spatial model Spatial model is learned implicitly by the classifiers in a data-driven fashion 2016/8/11 21
  • 22.
    Learning Feature Representations •Convolutional Architectures for Feature Embedding 2016/8/11 22
  • 23.
    Learning Context Representations •Large Receptive Fields as a Design Criterion 2016/8/11 23
  • 24.
    Learning Context Representations •Large Receptive Fields Improve Pose Estimation 2016/8/11 24
  • 25.
    Convolutional Pose Machines •Designing a Convolutional Architecture 2016/8/11 25
  • 26.
    Learning • Joint Trainingwith Intermediate Supervision 𝑓𝑡 = − 2 2 Loss: Euclidean distance groundtruth prediction Network without Intermediate Supervision leads vanishing gradients 2016/8/11 26
  • 27.
    Input Stage 1 Layer1 Layer 3 Layer 6 4 1 10 3 10 Epoch 10 2 1 10 0 10 Output Layer 18 Stage 2 Layer 7 Layer 9 Layer 12 Layer 13 Stage 3 Layer 15 4 2 10 3 10 Epoch 10 2 1 10 0 10 4 3 10 3 10 Epoch 10 2 1 10 0 10 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 Supervision Supervision Histograms of Gradient Magnitude During Training Supervision Learning Intermediate Supervision Addresses Vanishing Gradients Gradient Magnitude 10 Gradient (× 10−3 ) With Intermediate Supervision Without Intermediate Supervision 0 10 1 10 2 10 3 10 4 Input Image h w 3 5⇥5 C 5⇥5 C 2⇥ 5⇥5 9⇥9 1⇥1 1⇥1 P C C C C 9⇥9 C 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 2⇥ P 5⇥5 C 5⇥5 C 5⇥5 C 2⇥ P 2⇥ P Input Image h w 3 h0 w0 P1+1 P1+1 9⇥9 C Loss 1 f 2 Loss 1f 1 x1 1 x1 2 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 5⇥5 2⇥ 5⇥5 2⇥ 5⇥5 C P C P C Input Image h w 3 h0 w0 P1+1 Loss 1f 3 x1 2 h0 w0 Stage 3, level 1Stage 2, level 1Stage 1, level 1 2016/8/11 27
  • 28.
    Input Layer 1 Output Layer 18 10 0 10 1 10 2 10 3 10 4 Epoch1 Stage1 Layer 3 Layer 6 Layer 7 Stage 2 Layer 9 Layer 12 Layer 13 Stage 3 Layer 15 10 0 10 1 10 2 10 3 10 4 Epoch2 −0.5 0.0 0.5 10 0 10 1 10 2 10 3 10 4 Epoch3 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5 Histograms of Gradient Magnitude During Training Supervision Supervision Supervision Input Image h w 3 5⇥5 C 5⇥5 C 2⇥ 5⇥5 9⇥9 1⇥1 1⇥1 P C C C C 9⇥9 C 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 2⇥ P 5⇥5 C 5⇥5 C 5⇥5 C 2⇥ P 2⇥ P Input Image h w 3 h0 w0 P1+1 P1+1 9⇥9 C Loss 1 f 2 Loss 1f 1 x1 1 x1 2 9⇥9 13⇥13 13⇥13 15⇥15 1⇥1 1⇥1 C C C C C C 5⇥5 2⇥ 5⇥5 2⇥ 5⇥5 C P C P C Input Image h w 3 h0 w0 P1+1 Loss 1f 3 x1 2 h0 w0 Gradient (× 10−3 ) With Intermediate Supervision Without Intermediate Supervision Stage 3, level 1Stage 2, level 1Stage 1, level 1 Learning Intermediate Supervision Addresses Vanishing Gradients 2016/8/11 28
  • 29.
    0 0 Detectionrate% (i) With IntermediateSupervision (IS) (ii) Stagewise (iii) IS + Stagewise Pretrain (iv) Without Intermediate Supervision 0.05 0.1 0.15 0.2 Normalized distance 100 90 80 70 60 50 40 30 20 10 PCK total, LSP OC Learning Comparison of Learning Methods 2016/8/11 29
  • 30.
  • 31.
    Evaluation Qualitative Examples onLEEDS (Person-centric) 2016/8/11 L b31
  • 32.
    Evaluation Qualitative Examples onMPI (Person-centric) 2016/8/11 L b32
  • 33.
    Resolving Symmetric Confusions LeftRight t= 1 t = 2 t = 3 Wrists 2016/8/11 L b33
  • 34.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 34
  • 35.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Stage II Stage I Stage III Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Context from the confidence map of head is removed Ablative Spatial Analysis 2016/8/11 35
  • 36.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 36
  • 37.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 37
  • 38.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) StageIIStageIStageIII Predicted Pose Ablative Spatial Analysis 2016/8/11 38
  • 39.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 39
  • 40.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 40
  • 41.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 41
  • 42.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 42
  • 43.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 43
  • 44.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 44
  • 45.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 45
  • 46.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 46
  • 47.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 47
  • 48.
    Head Neck L.Sho.L.Elb. R.Hip R.Knee R.Ank. Bkgd. Predicted Pose Level 1 Part Confidences L.Wri. R.Sho. R.Elb. R.Wri. L.Hip L.Knee L.Ank. Predicted confidences are resilient to missing context (of one part) Stage II Stage I Stage III Ablative Spatial Analysis 2016/8/11 48
  • 49.
    0 0.05 0.10.15 Normalized distance 0.2 0 0 100 90 80 70 60 50 40 30 20 10 Detectionrate% Ours 3−Stage 2−Level Tompson et al., CVPR’15 Tompson et al., NIPS’14 Chen&Yullie, NIPS’14 Toshev et al., CVPR’14 Sapp et al., CVPR’13 Evaluation PCK Performance Comparison on FLIC dataset PCK wrist, FLIC 0.05 0.1 0.15 Normalized distance 0.2 PCK elbow, FLIC 2016/8/11 49
  • 50.
    0 0.05 0.10.15 Normalized distance Ours 3−Stage 2−Level 0.2 0 0 100 90 80 70 60 50 40 30 20 10 PCK total, LSP PC Detectionrate% Tompson et al., NIPS’14 Pishchulin et al., ICCV’13 Chen&Yuille, NIPS’14 Wang et al., CVPR’13 0.05 0.1 0.15 0.2 0 Normalized distance 0.05 0.1 0.15 0.2 0 Normalized distance PCK wrist&elbow, LSP PC 0.05 0.1 0.15 0.2 0 Normalized distance PCK knee, LSP PC 0.05 0.1 0.15 0.2 PCK ankle, LSP PC Normalized distance PCK hip, LSP PC Evaluation PCK Performance Comparison on LEEDS dataset (Person-centric) 2016/8/11 50