LDPose: Towards Inclusive Human Pose Estimation for Limb-Deficient Individuals in the Wild

Human pose estimation aims to predict the location of body keypoints and enable various practical applications. However, existing research focuses solely on individuals with full physical bodies and overlooks those with limb deficiencies. As a result, current pose estimation methods cannot be generalized to individuals with limb deficiencies. In this paper, we introduce the Limb-Deficient Pose Estimation task, which not only predicts the locations of standard human body keypoints, but also estimates the endpoints of missing limbs. To support this task, we present Limb-Deficient Pose (LDPose), the first-ever human pose estimation dataset for individuals with limb deficiencies. LDPose comprises over 28k images for approximately 73k individuals across di-verse limb deficiency types and ethnic backgrounds. The annotation process is guided by internationally accredited para-athletics classifiers to ensure high precision. In addition, we propose a Limb-Deficient Loss (LDLoss) to better distinguish residual limb keypoints by contrasting residual limb keypoints and intact limb keypoints. Furthermore, we design Limb-Deficient Metrics (LD Metrics) to quantitatively measure the keypoint predictions of both residual and intact limbs and benchmark our dataset using state-of-theart human pose estimation methods. Experiment results indicate that LDPose is a challenging dataset, and we believe that it will foster further research and ultimately support individuals with limb deficiencies worldwide. LDPose This ICCV paper is the Open Access version, provided by the Computer Vision Foundation.

Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.