DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
Cédric Rommel Eduardo Valle Mickaël Chen Souhaiel Khalfaoui Renaud Marlet Matthieu Cord Patrick Pérez
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by
integrating cutting-edge diffusion models, which have revolutionized diverse
fields, but are relatively unexplored in 3D-HPE. We show that diffusion models
enhance the accuracy, robustness, and coherence of human pose estimations. We
introduce DiffHPE, a novel strategy for harnessing diffusion models in 3D-HPE,
and demonstrate its ability to refine standard supervised 3D-HPE. We also show
how diffusion models lead to more robust estimations in the face of occlusions,
and improve the time-coherence and the sagittal symmetry of predictions. Using
the Human\,3.6M dataset, we illustrate the effectiveness of our approach and
its superiority over existing models, even under adverse situations where the
occlusion patterns in training do not match those in inference. Our findings
indicate that while standalone diffusion models provide commendable
performance, their accuracy is even better in combination with supervised
models, opening exciting new avenues for 3D-HPE research.