Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
This addresses high-fidelity 3D face modeling for applications like film production or biometrics, though it appears incremental by combining existing techniques.
The paper tackles 3D face reconstruction from sparse images by using morphable models to calibrate lighting and semantic segmentation to refine skin regions, achieving reconstruction of fine details like wrinkles and pores comparable to movie quality with very few images.
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image. We observe that 3D morphable faces approach provides a reasonable geometry proxy for light position calibration. Specifically, we develop a robust optimization technique that can calibrate per-pixel lighting direction and illumination at a very high precision without assuming uniform surface albedos. Next, we apply semantic segmentation on input images and the geometry proxy to refine hairy vs. bare skin regions using tailored filters. Experiments on synthetic and real data show that by using a very small set of images, our technique is able to reconstruct fine geometric details such as wrinkles, eyebrows, whelks, pores, etc, comparable to and sometimes surpassing movie quality productions.