DeepAI AI Chat
Log In Sign Up

PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation

by   Kehong Gong, et al.
National University of Singapore

Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator. Specifically, PoseAug introduces a novel pose augmentor that learns to adjust various geometry factors (e.g., posture, body size, view point and position) of a pose through differentiable operations. With such differentiable capacity, the augmentor can be jointly optimized with the 3D pose estimator and take the estimation error as feedback to generate more diverse and harder poses in an online manner. Moreover, PoseAug introduces a novel part-aware Kinematic Chain Space for evaluating local joint-angle plausibility and develops a discriminative module accordingly to ensure the plausibility of the augmented poses. These elaborate designs enable PoseAug to generate more diverse yet plausible poses than existing offline augmentation methods, and thus yield better generalization of the pose estimator. PoseAug is generic and easy to be applied to various 3D pose estimators. Extensive experiments demonstrate that PoseAug brings clear improvements on both intra-scenario and cross-scenario datasets. Notably, it achieves 88.6 on MPI-INF-3DHP under cross-dataset evaluation setup, improving upon the previous best data augmentation based method by 9.1


CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations

To improve the generalization of 3D human pose estimators, many existing...

PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning

3D pose estimation has recently gained substantial interests in computer...

VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data

While monocular 3D pose estimation seems to have achieved very accurate ...

DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation

Due to the lack of diversity of datasets, the generalization ability of ...

Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation

When applying a pre-trained 2D-to-3D human pose lifting model to a targe...

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

This paper addresses the problem of cross-dataset generalization of 3D h...

3D Human Pose Lifting with Grid Convolution

Existing lifting networks for regressing 3D human poses from 2D single-v...