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This study examines the association between financial education and budgeting behavior among college students. Under the guidance of the extended theory of planned behavior, we use a comprehensive measure of budgeting behavior and explore mediating factors between financial education and budgeting behavior.

Financial education was measured by both frequency and intensity of taking courses in finance and economics in college. Data from a sample of college students across China were analyzed using structural equation modeling and serial mediation analysis to explore the mediating roles of attitudes, subjective norms, perceived control and budgeting intentions in this relationship between financial education and budgeting behavior.

Budgeting intentions alone did not mediate the relationship between financial education and budgeting behavior. However, the serial mediation involving attitudes, subjective norms and budgeting intentions was significant.

The findings of this study have significant implications for financial educators, universities, governments and families. Financial educators should prioritize budgeting in curricula and aim to enhance students’ budgeting attitudes and intentions. Universities should enhance their financial education offerings, while governments and families should foster supportive environments and positive norms and attitudes around budgeting.

This research contributes a nuanced measurement of budgeting, analyzes the link between financial education and budgeting behavior among college students and highlights the roles of various components of the theory of planned behavior. It extends the theory by identifying how financial attitudes, subjective norms and budgeting intentions mediate the relationship between financial education and budgeting behavior.

This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images.

The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses.

The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot.

A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.