تقرير
Noise-in, Bias-out: Balanced and Real-time MoCap Solving
| العنوان: | Noise-in, Bias-out: Balanced and Real-time MoCap Solving |
|---|---|
| المؤلفون: | Albanis, Georgios, Zioulis, Nikolaos, Thermos, Spyridon, Chatzitofis, Anargyros, Kolomvatsos, Kostas |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning |
| الوصف: | Real-time optical Motion Capture (MoCap) systems have not benefited from the advances in modern data-driven modeling. In this work we apply machine learning to solve noisy unstructured marker estimates in real-time and deliver robust marker-based MoCap even when using sparse affordable sensors. To achieve this we focus on a number of challenges related to model training, namely the sourcing of training data and their long-tailed distribution. Leveraging representation learning we design a technique for imbalanced regression that requires no additional data or labels and improves the performance of our model in rare and challenging poses. By relying on a unified representation, we show that training such a model is not bound to high-end MoCap training data acquisition, and exploit the advances in marker-less MoCap to acquire the necessary data. Finally, we take a step towards richer and affordable MoCap by adapting a body model-based inverse kinematics solution to account for measurement and inference uncertainty, further improving performance and robustness. Project page: https://moverseai.github.io/noise-tail Comment: Project page: https://moverseai.github.io/noise-tail |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2309.14330 |
| رقم الأكسشن: | edsarx.2309.14330 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |