• Training: Given a pair of frames from the same video—a labeled Frame A and an unlabeled Frame B—we train our model to detect pose in Frame A using the features from Frame B.

I. Introduction

Learning Temporal Pose Estimation from Sparsely Labeled VideosGedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani

• Goal: Pose detection in video.• Challenge: Densely labeling every frame with multi-person pose

annotations is costly and time consuming.• Idea: Leverage sparsely annotated videos, i.e., pose annotations are

given only every k frames.

II. The PoseWarper Network

• We use dilated deformable convolutions to warp pose heatmaps from an unlabeled Frame B for pose prediction in a labeled frame A.

• Our model implicitly learns motion cues between Frame A and Frame B (no ground truth alignment is available between frames).

III. Applications of the PoseWarper Network1. Video Pose Propagation• Goal: Propagate ground truth pose annotations across the entire

video from only a few labeled frames.

Ground Truth Reference Frame (time t) PoseWarper (time t+1) FlowNet2 (time t+1)

3. Temporal Pose Aggregation at Inference• Challenge: Detecting pose in video can be difficult due to nuisance

effects such as occlusion, motion blur, and video defocus.• Goal: Improve detection by using pose information from adjacent frames.

• We train our PoseWarper model on the full PoseTrack dataset, i.e., when frames in videos are densely labeled.

• During inference, we use our temporal pose aggregation scheme to aggregate information from 5 nearby frames.

2. Data Augmentation with PoseWarper• Goal: Augment sparsely labeled video data with our propagated

poses. Then, train an HRNet-W48 pose detector on this joint data.

Comparison to State-of-the-Art

IV. Additional Experiments

Interpreting our Learned Offsets• It appears that different offset maps encode different motion, thus performing a

sort of motion decomposition of discriminative regions in the video.

V. ConclusionsFrame t Frame t+5 Offset MagnitudesChannel 99 (x,y)Channel 123 (x,y)

• Our approach reduces the need for densely labeled video data, while producing strong pose detection performance.

• PoseWarper is useful even when training videos are densely labeled.• The source code and our trained models will be made publicly available at:

https://github.com/facebookresearch/PoseWarper

• Application Stage: After training, we can apply our model in reverse order to propagate pose information across the training video from sparse ground truth poses.

2. Application Stage (Labeled Unlabeled)

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Unlabeled Frame B (time t)

Ground Truth Pose Propagated to Frame B

Ground Truth Pose in Frame A

Features Relating the Two Frames

Feature Warping

Architecture

• Every 7 th frame in a training video is labeled. We propagate pose annotations from manually-labeled frames to all unlabeled frames in the same video.

• Our temporal pose aggregation scheme during inference is another effective way to maintain strong performance in a sparsely-labeled video setting.

Warping from A to B

Pose Heatmap B

Frame B (time t)Frame A (time t-1) Frame C (time t+1)

Pose Heatmap C Pose Heatmap A

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Warping from C to B

Pose B (time t)

0 2 4 6 8

# Labeled Frames Per Video

70

75

80

Acc

ura

cy (

mA

P)

GT (7x)GT (1x) + pGT (6x)GT (1x)

0 100 200 300

# Training Videos

70

75

80

Acc

ura

cy (

mA

P)

GT (7x)GT (1x) + pGT (6x)GT (1x)

0 2 4 6 8

# Labeled Frames Per Video

70

75

80

Acc

ura

cy (

mA

P)

GT (7x)GT (1x) + ST-Agg.GT (1x)

0 100 200 300

# Training Videos

70

75

80

Acc

ura

cy (

mA

P)

GT (7x)GT (1x) + ST-Agg.GT (1x)

A Labeled Frame (time t)An Unlabeled Frame (time t-1) An Unlabeled Frame (time t+1)

A Sparsely Labeled Multi-Person Video