Alive Caricature from 2D to 3D
This addresses the challenge of generating 3D caricatures for artists or applications in entertainment and design, offering a more flexible method without needing complex 3D training sets.
This paper tackles the problem of creating expressive 3D caricatures from 2D images with minimal user interaction, achieving better capability in expressing caricatures compared to existing fitting approaches like 3DMM and FaceWareHouse.
Caricature is an art form that expresses subjects in abstract, simple and exaggerated view. While many caricatures are 2D images, this paper presents an algorithm for creating expressive 3D caricatures from 2D caricature images with a minimum of user interaction. The key idea of our approach is to introduce an intrinsic deformation representation that has a capacity of extrapolation enabling us to create a deformation space from standard face dataset, which maintains face constraints and meanwhile is sufficiently large for producing exaggerated face models. Built upon the proposed deformation representation, an optimization model is formulated to find the 3D caricature that captures the style of the 2D caricature image automatically. The experiments show that our approach has better capability in expressing caricatures than those fitting approaches directly using classical parametric face models such as 3DMM and FaceWareHouse. Moreover, our approach is based on standard face datasets and avoids constructing complicated 3D caricature training set, which provides great flexibility in real applications.