Hierarchical MLANet: Multi-level Attention for 3D Face Reconstruction From Single Images

• URL: http://arxiv.org/abs/2509.10024v3
• Date: Sun, 28 Sep 2025 07:39:06 GMT
• Title: Hierarchical MLANet: Multi-level Attention for 3D Face Reconstruction From Single Images
• Authors: Danling Cao,
• Abstract summary: We propose a convolutional neural network-based approach for reconstructing 3D face models from single in-the-wild images.<n>Our model predicts detailed facial geometry, texture, pose, and illumination parameters from a single image.<n>A semi-supervised training strategy is employed, incorporating 3D Morphable Model (3DMM) parameters from publicly available datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering 3D face models from 2D in-the-wild images has gained considerable attention in the computer vision community due to its wide range of potential applications. However, the lack of ground-truth labeled datasets and the complexity of real-world environments remain significant challenges. In this chapter, we propose a convolutional neural network-based approach, the Hierarchical Multi-Level Attention Network (MLANet), for reconstructing 3D face models from single in-the-wild images. Our model predicts detailed facial geometry, texture, pose, and illumination parameters from a single image. Specifically, we employ a pre-trained hierarchical backbone network and introduce multi-level attention mechanisms at different stages of 2D face image feature extraction. A semi-supervised training strategy is employed, incorporating 3D Morphable Model (3DMM) parameters from publicly available datasets along with a differentiable renderer, enabling an end-to-end training process. Extensive experiments, including both comparative and ablation studies, were conducted on two benchmark datasets, AFLW2000-3D and MICC Florence, focusing on 3D face reconstruction and 3D face alignment tasks. The effectiveness of the proposed method was evaluated both quantitatively and qualitatively.

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