MoCapDT: Temporal Guided Motion Capture Solving with Diffusion Transfer

Abstract

We present an approach to reconstruct the joint location from noisy marker position in 4D data. The 4D data means the 3D location of different markers and time sequence. At the core of our approach, we apply a modified diffusion model architecture to transfer and denoise the raw marker information in the latent space under the guidance of other temporal data. Then we decode the latent space to not real skeleton 3D space. This enable us not only utilize the temporal guidance, we further utilize the iterative denoising technique to exploit the potential in the diffusion network. Furthermore, we demonstrate that our work outperform auto- encoder based deep learning model by large margin during our experiment on CMU-Synthesized data set and some real-world dataset provided by NC-SOFT.