Coupled 3D Reconstruction of Sparse Facial Hair and Skin
đź“ś Abstract
We present a novel framework to reconstruct a 3D model of facial hair along with the skin surface underneath from a small number of unconstrained images of a person captured in-the-wild. Our reconstruction method is the first to jointly recover and refine the skin and sparse facial hair geometry from just a few images. To solve this challenging problem, we develop a pipeline of analysis tools consisting of filter-based image processing, optical flow estimation for hair motion, a min-cut optimization for extracting the hair from the background, and a coupled 3D morphable model for hair and skin refinement. Additionally, we introduce a new dataset of facial images with annotated 3D facial hair models. Experimental results demonstrate that our method provides high-quality results for 3D reconstructions of facial hair and underlying skin in unconstrained environments, outperforming state-of-the-art techniques.
✨ Summary
The paper “Coupled 3D Reconstruction of Sparse Facial Hair and Skin” introduces an innovative framework focused on the 3D reconstruction of facial hair and skin surfaces from a limited set of images captured in real-world, uncontrolled settings. This is achieved through a sequence of image analysis techniques including filter-based image processing, optical flow estimation, and a min-cut optimization method, culminating in a coupled 3D morphable model that refines both facial hair and skin. The work is notable for being the first to address the simultaneous reconstruction and refinement of sparse facial hair along with skin geometry using just a few input images.
A significant contribution of the paper is the introduction and demonstration of a new dataset of facial images accompanied by annotated 3D models of facial hair, which may serve as a valuable resource for future research. The experimental results shared in the paper showcase that this approach delivers high-quality 3D reconstructions that surpass current state-of-the-art methodologies.
Despite extensive searches, explicit mentions or citations of the impact of this particular paper in subsequent research or industry applications were not found, indicating it either hasn’t been widely cited yet or its impact is localized within specific research projects or commercial applications not readily accessible in the literature. The potential of its methodologies and dataset might be recognized in long-term developments in fields like digital human generation and computer graphics. Given its novel approach, it might be gaining traction in related spheres of technological research and industry.