We introduce a novel camera model for monocular 3D Morphable Model (3DMM) regression methods that effectively captures the perspective distortion effect commonly seen in close-up facial images. Fitting 3D morphable models to video is a key technique in content creation. In particular, regression-based approaches have produced fast and accurate results by matching the rendered output of the morphable model to the target image. These methods typically achieve stable performance with orthographic projection, which eliminates the ambiguity between focal length and object distance. However, this simplification makes them unsuitable for close-up footage, such as that captured with head-mounted cameras. We extend orthographic projection with a new shrinkage parameter, incorporating a pseudo-perspective effect while preserving the stability of the original projection. We present several techniques that allow finetuning of existing models, and demonstrate the effectiveness of our modification through both quantitative and qualitative comparisons using a custom dataset recorded with head-mounted cameras.

翻译：我们为单目三维可变形模型回归方法引入了一种新颖的相机模型，该模型能有效捕捉近距离面部图像中常见的透视畸变效应。将三维可变形模型拟合至视频是内容创作中的一项关键技术。特别是，基于回归的方法通过将可变形模型的渲染输出与目标图像进行匹配，已能产生快速而准确的结果。这些方法通常采用正交投影以实现稳定的性能，从而消除了焦距与物距之间的模糊性。然而，这种简化使其不适用于近距离拍摄的影像，例如使用头戴式摄像机捕获的画面。我们通过引入一个新的收缩参数扩展了正交投影，在保持原始投影稳定性的同时融入了伪透视效果。我们提出了多种技术，允许对现有模型进行微调，并通过使用头戴式摄像机记录的自定义数据集进行定量与定性比较，证明了我们改进方案的有效性。