When working with 3D facial data, improving fidelity and avoiding the uncanny valley effect is critically dependent on accurate 3D facial performance capture. Because such methods are expensive and due to the widespread availability of 2D videos, recent methods have focused on how to perform monocular 3D face tracking. However, these methods often fall short in capturing precise facial movements due to limitations in their network architecture, training, and evaluation processes. Addressing these challenges, we propose a novel face tracker, FlowFace, that in-troduces an innovative 2D alignment network for dense pervertex alignment. Unlike prior work, FlowFace is trained on high-quality 3D scan annotations rather than weak supervision or synthetic data. Our 3D model fitting module Jointly fits a 3D face model from one or many observations, integrating existing neutral shape priors for enhanced identity and expression disentanglement and per-vertex de-formations for detailed facial feature reconstruction. Additionally, we propose a novel metric and benchmark for assessing tracking accuracy. Our method exhibits superior performance on both custom and publicly available bench-marks. We further validate the effectiveness of our tracker by generating high-quality 3D data from 2D videos, which leads to performance gains on downstream tasks.

We consider the problem of weakly supervised object localization. For an object of interest (e.g. “car”), an image is weakly labeled when its label only indicates the presence/absence of this object, but not the exact location of the object in the image. Given a collection of weakly labeled images for an object, our goal is to localize the object of interest in each image. We propose a novel architecture called the regularized attention network for this problem. Our work builds upon the attention network proposed in [1]. We extend the standard attention network by incorporating a regularization term that encourages the attention scores of object proposals to mimic the scoring distribution of a strong fully supervised object detector. Despite of the simplicity of our approach, our proposed architecture achieves the state-of-the-art results on several benchmark datasets.