The research fields of parametric face models and 3D face reconstruction have been extensively studied. However, a critical question remains unanswered: how to tailor the face model for specific reconstruction settings. We argue that reconstruction with multi-view uncalibrated images demands a new model with stronger capacity. Our study shifts attention from data-dependent 3D Morphable Models (3DMM) to an understudied human-designed skinning model. We propose Adaptive Skinning Model (ASM), which redefines the skinning model with more compact and fully tunable parameters. With extensive experiments, we demonstrate that ASM achieves significantly improved capacity than 3DMM, with the additional advantage of model size and easy implementation for new topology. We achieve state-of-the-art performance with ASM for multi-view reconstruction on the Florence MICC Coop benchmark. Our quantitative analysis demonstrates the importance of a high-capacity model for fully exploiting abundant information from multi-view input in reconstruction. Furthermore, our model with physical-semantic parameters can be directly utilized for real-world applications, such as in-game avatar creation. As a result, our work opens up new research directions for the parametric face models and facilitates future research on multi-view reconstruction.

翻译：参数化人脸模型与三维人脸重建的研究领域已得到广泛探索。然而，一个关键问题仍未得到解答：如何针对特定重建场景定制人脸模型。我们认为，利用多视角未标定图像进行重建需要一种具有更强表达能力的新模型。本研究将关注点从依赖数据的可形变模型转向尚未被充分研究的人为设计的蒙皮模型。我们提出自适应蒙皮模型，该模型以更紧凑且完全可调参数重新定义了蒙皮模型。通过大量实验，我们证明ASM在模型容量上显著优于3DMM，同时具备模型尺寸更小、易于实现新拓扑结构的额外优势。在Florence MICC Coop基准测试的多视角重建任务中，我们利用ASM取得了当前最优性能。定量分析表明，高容量模型对于在多视角输入重建中充分利用丰富信息至关重要。此外，具有物理语义参数的模型可直接用于实际应用，如游戏虚拟角色创建。因此，本研究为参数化人脸模型开辟了新的研究方向，并促进了多视角重建的未来研究。