Denoising Diffusion for 3D Hand Pose Estimation from Images
Maksym Ivashechkin Oscar Mendez Richard Bowden
Hand pose estimation from a single image has many applications. However,
approaches to full 3D body pose estimation are typically trained on day-to-day
activities or actions. As such, detailed hand-to-hand interactions are poorly
represented, especially during motion. We see this in the failure cases of
techniques such as OpenPose or MediaPipe. However, accurate hand pose
estimation is crucial for many applications where the global body motion is
less important than accurate hand pose estimation.
  This paper addresses the problem of 3D hand pose estimation from monocular
images or sequences. We present a novel end-to-end framework for 3D hand
regression that employs diffusion models that have shown excellent ability to
capture the distribution of data for generative purposes. Moreover, we enforce
kinematic constraints to ensure realistic poses are generated by incorporating
an explicit forward kinematic layer as part of the network. The proposed model
provides state-of-the-art performance when lifting a 2D single-hand image to
3D. However, when sequence data is available, we add a Transformer module over
a temporal window of consecutive frames to refine the results, overcoming
jittering and further increasing accuracy.
  The method is quantitatively and qualitatively evaluated showing
state-of-the-art robustness, generalization, and accuracy on several different
datasets.