3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
This addresses the need for efficient and detailed 3D face modeling in applications like computer vision and graphics, though it is incremental as it builds on existing color photometric stereo methods.
The paper tackles the problem of 3D face reconstruction from a single image using uncalibrated near point lights, achieving state-of-the-art results with high-fidelity geometry that captures details like wrinkles.
We present a new color photometric stereo (CPS) method that recovers high quality, detailed 3D face geometry in a single shot. Our system uses three uncalibrated near point lights of different colors and a single camera. For robust self-calibration of the light sources, we use 3D morphable model (3DMM) and semantic segmentation of facial parts. We address the spectral ambiguity problem by incorporating albedo consensus, albedo similarity, and proxy prior into a unified framework. We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile. Experiments show that our new approach produces state-of-the-art results from single image with high-fidelity geometry that includes details such as wrinkles.