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For facial recognition, textured three-dimensional (3D) meshes offer critical depth information that enhance identification across multiple perspectives. However, 3D facial recognition is often hindered by data limitations, collection environments, and domain shifts. Therefore, we propose a method to synthesize textured 3D facial meshes using existing two-dimensional (2D) face images. Our method demonstrates improved pose invariance by synthesizing faces and leveraging combinations of synthetic and real 3D facial data to improve facial recognition performance. Figure 1 provides an example of synthesized textured meshes from the Facescape1 dataset, which includes 3D faces, textures, and corresponding 2D images. Through synthesizing 3D geometry and occluded/nonoccluded textures, this method leverages pose invariant features from textured 3D meshes using 2D imagery for complex facial recognition tasks. We implement a 2D-to-3D domain adaptation scheme that enables Adaface2—a leading 2D recognition framework—to discriminate 3D facial features learned from Pointnet++.3 This strengthens off-pose identification, highlighting the importance of data synthesis in expanding capabilities. Our proposed method improves pose invariance by leveraging denoising diffusion probabilistic models (DDPMs)4 conditioned on 2D representations to construct 3D textured meshes. This approach presents a robust alternative to existing methodologies,5, 6 emphasizing the advantages of 2D networks to infer 3D features for enhanced recognition. Leveraging DDPMs and domain adaptation broadens the landscape for image recognition systems, signifying diverse data structures, even synthetic, for improving recognition performance under challenging conditions. The results demonstrate enhanced modeling to bridge the gap between 3D meshes and images.
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