  Three-dimensional (3D) human pose estimation using a monocular camera has
gained increasing attention due to its ease of implementation and the abundance
of data available from daily life. However, owing to the inherent depth
ambiguity in images, the accuracy of existing monocular camera-based 3D pose
estimation methods remains unsatisfactory, and the estimated 3D poses usually
include much noise. By observing the histogram of this noise, we find each
dimension of the noise follows a certain distribution, which indicates the
possibility for a neural network to learn the mapping between noisy poses and
ground truth poses. In this work, in order to obtain more accurate 3D poses, a
Diffusion-based 3D Pose Refiner (D3PRefiner) is proposed to refine the output
of any existing 3D pose estimator. We first introduce a conditional
multivariate Gaussian distribution to model the distribution of noisy 3D poses,
using paired 2D poses and noisy 3D poses as conditions to achieve greater
accuracy. Additionally, we leverage the architecture of current diffusion
models to convert the distribution of noisy 3D poses into ground truth 3D
poses. To evaluate the effectiveness of the proposed method, two
state-of-the-art sequence-to-sequence 3D pose estimators are used as basic 3D
pose estimation models, and the proposed method is evaluated on different types
of 2D poses and different lengths of the input sequence. Experimental results
demonstrate the proposed architecture can significantly improve the performance
of current sequence-to-sequence 3D pose estimators, with a reduction of at
least 10.3% in the mean per joint position error (MPJPE) and at least 11.0% in
the Procrustes MPJPE (P-MPJPE).
