Bei Xiao

This study explores the relationship between visual perception and language in material recognition using psychophysics and unsupervised learning, finding moderate correlation but structural differences in representations.

This paper uses an unsupervised image generation model to discover latent features related to material perception, finding that translucency is encoded in mid-scale features.

This study investigates how color affects perception of translucent materials using real-world object photographs, finding that color improves agreement and accuracy in material classification and attribute ratings.

This study investigates how geometric sharpness influences human perception of translucency in materials, finding that sharp features significantly affect perceived translucency.

The paper shows that manipulating dynamic deformation patterns can alter perceived cloth stiffness, identifying specific optical flow features that correlate with stiffness perception.

This study investigates how static visual cues and force-feedback interact to influence the perception of mass in virtual objects and shows that visual appearance affects both perception and manipulation kinematics.

This paper shows that humans and machine learning models use multi-frame motion information to estimate cloth stiffness from videos.