3DGStrands: Personalized 3D Gaussian splatting for realistic hair representation and animation

Abstract

We introduce a novel method for generating a personalized 3D Gaussian Splatting (3DGS) hair representation from an unorganized set of photographs. Our approach begins by leveraging an out-of-the-shelf method to estimate a strand-organized point cloud representation of the hair. This point cloud serves as the foundation for constructing a 3DGS model that accurately preserves the hair’s geometric structure while visually fitting the appearance in the photographs. Our model seamlessly integrates with the standard 3DGS rendering pipeline, enabling efficient volumetric rendering of complex hairstyles. Furthermore, we demonstrate the versatility of our approach by applying the Material Point Method (MPM) to simulate realistic hair physics directly on the 3DGS model, achieving lifelike hair animation. To the best of our knowledge, this is the first method to simulate hair dynamics within a 3DGS model. This work paves the way for future research that can leverage the flexible nature of 3DGS to fit more complex hair material models or enable physics properties estimation through dynamic tracking.