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AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation

CVPR 2022 · 3D vision › Human pose estimation · approach: Monocular 2D-to-3D pose lifting · 34 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
adapt generated pose and camera distributions to the target domainVideoPose3D with AdaptPose · 3DHP testMPJPE: 96.4 -> 77.2Helped
add bone generation (ablation)AdaptPose · 3DHP testMPJPE: 88.1 -> 77.2Helped
add camera generation (ablation)AdaptPose · 3DHP testMPJPE: 96.4 -> 88.2Helped
add the domain discriminator for target-domain adaptation (ablation)AdaptPose with bone and camera generation · 3DHP testMPJPE: 88.1 -> 78.6Helped
add sample selection (ablation)AdaptPose with bone generation, camera generation, and domain discriminator · 3DHP testMPJPE: 78.6 -> 77.2Helped
use BG3 axis-angle bone rotation instead of BG1 bone displacement generation (ablation)AdaptPose · 3DHP testMPJPE: 85.1 -> 77.2Helped
use BG3 axis-angle bone rotation instead of BG2 bone generation (ablation)AdaptPose · 3DHP testMPJPE: 80.0 -> 77.2Helped
learn a probabilistic camera distribution instead of one deterministic camera (ablation)AdaptPose · 3DHP testMPJPE: 82.8 -> 77.2Helped
use probabilistic axis-angle rather than quaternion camera representation (ablation)AdaptPose · 3DHP testMPJPE: 83.5 -> 77.2Helped
use probabilistic axis-angle rather than Euler-angle camera representation (ablation)AdaptPose · 3DHP testMPJPE: 85.3 -> 77.2Helped
remove temporal input and use single-frame pairs (ablation)AdaptPose · 3DHP testMPJPE: 77.2 -> 86.4Hurt
use AdaptPose rather than BOAcross-dataset pose estimation · 3DPW testMPJPE: 77.2 -> 81.2Hurt

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