RopeTP: Global Human Motion Recovery via Integrating Robust Pose Estimation with Diffusion Trajectory Prior

This supplementary material offers additional experimental results and in-depth explanations to augment the content of the main paper. In Section 1, we elaborate extensively on the preliminary work. Subsequently, in Section 2, we delve into the implementation details of the Rope module. Following this, in Section 3, we provide a wealth of experimental data and model parameters pertaining to the effectiveness of the Rope module, facilitating a more intuitive comparison. Furthermore, we supply a comprehensive visualization of the attention results and corresponding analysis. Lastly, we present a plethora of challenging data to compare and validate the efficacy of our method in handling real-world occlusion issues, and discuss its limitations and instances of failure. Beyond this, we further elucidate the details of TrajDenoiser and provide prospects for applications in 3D space.