  In recent times, there has been a growing interest in developing effective
perception techniques for combining information from multiple modalities. This
involves aligning features obtained from diverse sources to enable more
efficient training with larger datasets and constraints, as well as leveraging
the wealth of information contained in each modality. 2D and 3D Human Pose
Estimation (HPE) are two critical perceptual tasks in computer vision, which
have numerous downstream applications, such as Action Recognition,
Human-Computer Interaction, Object tracking, etc. Yet, there are limited
instances where the correlation between Image and 2D/3D human pose has been
clearly researched using a contrastive paradigm. In this paper, we propose
UniHPE, a unified Human Pose Estimation pipeline, which aligns features from
all three modalities, i.e., 2D human pose estimation, lifting-based and
image-based 3D human pose estimation, in the same pipeline. To align more than
two modalities at the same time, we propose a novel singular value based
contrastive learning loss, which better aligns different modalities and further
boosts the performance. In our evaluation, UniHPE achieves remarkable
performance metrics: MPJPE $50.5$mm on the Human3.6M dataset and PAMPJPE
$51.6$mm on the 3DPW dataset. Our proposed method holds immense potential to
advance the field of computer vision and contribute to various applications.
