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.

Abstract Estimating fabric mechanical properties is crucial to create realistic digital twins. Existing methods typically require testing physical fabric samples with expensive devices or cumbersome capture setups. In this work, we propose a method to estimate fabric mechanics just from known manufacturer metadata such as the fabric family, the density, the composition, and the thickness. Further, to alleviate the need to know the fabric family –which might be ambiguous or unknown for nonspecialists–we propose an end‐to‐end neural method that works with planar images of the textile as input. We evaluate our methods using extensive tests that include the industry standard Cusick and demonstrate that both of them produce drapes that strongly correlate with the ground truth estimates provided by lab equipment. Our method is the first to propose such a simple capture method for mechanical properties outperforming other methods that require testing the fabric in specific setups.

We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be ill-posed –more than a single diffuse image might be needed to disambiguate the specular reflection– or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment.