A Transfer Learning Approach for Diverse Motion Augmentation Under Data Scarcity

A Transfer Learning Approach for Diverse Motion Augmentation Under Data Scarcity

Motion-capture data provide high accuracy but are difficult to obtain, necessitating dataset augmentation. To our knowledge, no prior study has investigated few-shot generative models for motion-capture data that address both quality and diversity. We tackle the diversity loss that arises with extre...

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Bibliographic Details
Main Authors: Junwon Yoon, Jeon-Seong Kang, Ha-Yoon Song, Beom-Joon Park, Kwang-Woo Jeon, Hyun-Joon Chung, Jang-Sik Park
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Mathematics
Subjects:
data augmentation
few-shot learning
generative adversarial networks
transfer learning
motion capture
elastic weight consolidation
Online Access:https://www.mdpi.com/2227-7390/13/15/2506
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