3D Face Hallucination from a Single Depth Frame
Shu Liang, Ira Kemelmacher-Shlizerman, Linda G. Shapiro

TL;DR
This paper introduces an automatic method to generate high-resolution 3D face models from a single depth image by matching and combining regional shapes from a large database, demonstrating robustness across various face types.
Contribution
The novel approach uses regional matching with a large 3D face database to produce detailed 3D face reconstructions from minimal depth data.
Findings
High-quality 3D face reconstructions from single depth frames.
Robustness across age, ethnicity, and facial expressions.
Outperforms existing shape estimation methods.
Abstract
We present an algorithm that takes a single frame of a person's face from a depth camera, e.g., Kinect, and produces a high-resolution 3D mesh of the input face. We leverage a dataset of 3D face meshes of 1204 distinct individuals ranging from age 3 to 40, captured in a neutral expression. We divide the input depth frame into semantically significant regions (eyes, nose, mouth, cheeks) and search the database for the best matching shape per region. We further combine the input depth frame with the matched database shapes into a single mesh that results in a high-resolution shape of the input person. Our system is fully automatic and uses only depth data for matching, making it invariant to imaging conditions. We evaluate our results using ground truth shapes, as well as compare to state-of-the-art shape estimation methods. We demonstrate the robustness of our local matching approach…
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Taxonomy
TopicsFace recognition and analysis · Advanced Vision and Imaging · 3D Shape Modeling and Analysis
