Transfer Learning based Parameterized 3D Mesh Deformation with 2D Stylized Cartoon Character

• Sanghyun Byun,
• Bumsoo Kim,
• Wonseop Shin,
• Yonghoon Jung,
• Sanghyun Seo,

Vol. 17, No. 11, November 30, 2023
10.3837/tiis.2023.11.012, Download Paper (Free):

• 3D modeling
• Mesh Deformation
• Computing methodologies
• 3D Stylized Avatar
• Metaverse Applications

Abstract

As interest in the metaverse has grown, there has been a demand for avatars that can represent individual users. Consequently, research has been conducted to reduce the time and cost required for the current 3D human modeling process. However, the recent automatic generation of 3D humans has been focused on creating avatars with a realistic human form. Furthermore, the existing methods have limitations in generating avatars with imbalanced or unrealistic body shapes, and their utilization is limited due to the absence of datasets. Therefore, this paper proposes a new framework for automatically transforming and creating stylized 3D avatars. Our research presents a definitional approach and methodology for creating non-realistic character avatars, in contrast to previous studies that focused on creating realistic humans. We define a new shape representation parameter and use a deep learning–based method to extract character body information and perform automatic template mesh transformation, thereby obtaining non-realistic or unbalanced human meshes. We present the resulting outputs visually, conducting user evaluations to demonstrate the effectiveness of our proposed method. Our approach provides an automatic mesh transformation method tailored to the growing demand for avatars of various body types and extends the existing method to the 3D cartoon stylized avatar domain.

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