Pixels2Points: Fusing 2D and 3D Features for Facial Skin Segmentation

• URL: http://arxiv.org/abs/2504.19718v1
• Date: Mon, 28 Apr 2025 12:13:12 GMT
• Title: Pixels2Points: Fusing 2D and 3D Features for Facial Skin Segmentation
• Authors: Victoria Yue Chen, Daoye Wang, Stephan Garbin, Sebastian Winberg, Timo Bolkart, Thabo Beeler,
• Abstract summary: Face registration deforms a template mesh to closely fit a 3D face scan, the quality of which commonly degrades in non-skin regions.<n>Existing image-based (2D) or scan-based (3D) segmentation methods however perform poorly.<n>We introduce a novel method that accurately separates skin from non-skin geometry on 3D human head scans.
• Score: 12.2258243244703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face registration deforms a template mesh to closely fit a 3D face scan, the quality of which commonly degrades in non-skin regions (e.g., hair, beard, accessories), because the optimized template-to-scan distance pulls the template mesh towards the noisy scan surface. Improving registration quality requires a clean separation of skin and non-skin regions on the scan mesh. Existing image-based (2D) or scan-based (3D) segmentation methods however perform poorly. Image-based segmentation outputs multi-view inconsistent masks, and they cannot account for scan inaccuracies or scan-image misalignment, while scan-based methods suffer from lower spatial resolution compared to images. In this work, we introduce a novel method that accurately separates skin from non-skin geometry on 3D human head scans. For this, our method extracts features from multi-view images using a frozen image foundation model and aggregates these features in 3D. These lifted 2D features are then fused with 3D geometric features extracted from the scan mesh, to then predict a segmentation mask directly on the scan mesh. We show that our segmentations improve the registration accuracy over pure 2D or 3D segmentation methods by 8.89% and 14.3%, respectively. Although trained only on synthetic data, our model generalizes well to real data.

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