Can I read DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization on EtoBox?

DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization by Ponton, Jose Luis; Pujol, Eduard; Aristidou, Andreas; Andujar, Carlos; Pelechano, Nuria is a scholarly article available to read on EtoBox.

What is DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization about?

High-quality motion reconstruction that follows the user's movements can be achieved by high-end mocap systems with many sensors. However, obtaining such animation quality with fewer input devices is gaining popularity as it brings mocap closer to the general public. The main challenges include the loss of end-effector accuracy in learning-based approaches, or the lack of naturalness and smoothness in IK-based solutions. In addition, such systems are often finely tuned to a specific number of trackers and are highly sensitive to missing data e.g., in scenarios where a sensor is occluded or malfunctions. In response to these challenges, we introduce DragPoser, a novel deep-learning-based motion reconstruction system that accurately represents hard and dynamic on-the-fly constraints, attaining real-time high end-effectors position accuracy. This is achieved through a pose optimization process within a structured latent space. Our system requires only one-time training on a large human motion dataset, and then constraints can be dynamically defined as losses, while the pose is iteratively refined by computing the gradients of these losses within the latent space. To further enhance ou

Author

Ponton, Jose Luis; Pujol, Eduard; Aristidou, Andreas; Andujar, Carlos; Pelechano, Nuria

Published

2024

Language

EN