Exploring Latent Cross-Channel Embedding for Accurate 3D Human Pose
  Reconstruction in a Diffusion Framework
Junkun Jiang Jie Chen
Monocular 3D human pose estimation poses significant challenges due to the
inherent depth ambiguities that arise during the reprojection process from 2D
to 3D. Conventional approaches that rely on estimating an over-fit projection
matrix struggle to effectively address these challenges and often result in
noisy outputs. Recent advancements in diffusion models have shown promise in
incorporating structural priors to address reprojection ambiguities. However,
there is still ample room for improvement as these methods often overlook the
exploration of correlation between the 2D and 3D joint-level features. In this
study, we propose a novel cross-channel embedding framework that aims to fully
explore the correlation between joint-level features of 3D coordinates and
their 2D projections. In addition, we introduce a context guidance mechanism to
facilitate the propagation of joint graph attention across latent channels
during the iterative diffusion process. To evaluate the effectiveness of our
proposed method, we conduct experiments on two benchmark datasets, namely
Human3.6M and MPI-INF-3DHP. Our results demonstrate a significant improvement
in terms of reconstruction accuracy compared to state-of-the-art methods. The
code for our method will be made available online for further reference.