Revisiting an Old Perspective Projection for Monocular 3D Morphable Models Regression

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 by using orthographic projection, which removes ambiguity related to focal length and object distance. However, this simplification makes them unsuitable for close-up footage, such as that captured with head-mounted cameras.To address this limitation, we introduce a novel shrinkage parameter to the orthographic projection, enabling the incorporation of a pseudo-perspective effect while preserving the stability of the original projection. We present several techniques that allow this parameter to be integrated into existing orthographic methods with minimal changes through fine-tuning. We demonstrate the effectiveness of our modification through both quantitative and qualitative comparisons using a custom dataset recorded with head-mounted cameras.