Sariyanidi, EvangelosZampella, Casey J.Drye, Madison N.Fecher, Madison L.Magginson, GraceCubit, Laura SoskeySchultz, Robert T.Guthrie, WhitneyTunc, BirkanBerretti, StefanoThehoaris, TheoharisDaoudi, MohamedFerrari, ClaudioVeltkamp, Remco C.2022-08-312022-08-312022978-3-03868-174-81997-0471https://doi.org/10.2312/3dor.20221178https://diglib.eg.org:443/handle/10.2312/3dor202211783D morphable models (3DMMs) simultaneously reconstruct facial morphology, expression and pose from 2D images, and thus could be an invaluable tool for capturing and characterizing the face and facial behavior in early childhood. However, 3DMM fitting on infants is a largely unexplored problem. All publicly available 3DMMs are developed for adults, and it is unclear if and to what extent they can be used on videos of infants. In this paper, we compare five state-of-the-art 3DMM fitting methods on data from naturalistic infant-caregiver interactions. Results suggest that it is possible to produce consistent and subject-specific reconstructions of 3D shape identity from multiple frames, but not from a single frame. Qualitative evaluation highlights that facial regions with high texture variation, such as eyes, brows and mouth, are captured with higher accuracy compared to the rest of the face. Thus, even though a 3DMM developed for adults has significant limitations when reconstructing the morphology of the entire facial region of infants, applications that involve analysis of facial behavior can be feasible. Our encouraging results, combined with the unique ability of 3DMMs to disentangle two major sources of noise for expression analysis (i.e., identity bias and pose variations), motivate future research on using 3DMMs to measure the facial behavior of infants.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Empirical studies in HCI; Computing methodologies → Shape representationsHuman centered computing → Empirical studies in HCIComputing methodologies → Shape representations10.2312/3dor.202211781-88 pages