Although tremendous strides have been made in uncontrolled face detection, efficient face detection with a low computation cost as well as high precision remains an open challenge. In this paper, we point out that training data sampling and computati
on distribution strategies are the keys to efficient and accurate face detection. Motivated by these observations, we introduce two simple but effective methods (1) Sample Redistribution (SR), which augments training samples for the most needed stages, based on the statistics of benchmark datasets; and (2) Computation Redistribution (CR), which reallocates the computation between the backbone, neck and head of the model, based on a meticulously defined search methodology. Extensive experiments conducted on WIDER FACE demonstrate the state-of-the-art efficiency-accuracy trade-off for the proposed scrfd family across a wide range of compute regimes. In particular, scrfdf{34} outperforms the best competitor, TinaFace, by $3.86%$ (AP at hard set) while being more than emph{3$times$ faster} on GPUs with VGA-resolution images. We also release our code to facilitate future research.
Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a sing
le in-the-wild image. Nevertheless, to the best of our knowledge, there is no method which can produce high-resolution photorealistic 3D faces from in-the-wild images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this paper, we introduce AvatarMe, the first method that is able to reconstruct photorealistic 3D faces from a single in-the-wild image with an increasing level of detail. To achieve this, we capture a large dataset of facial shape and reflectance and build on a state-of-the-art 3D texture and shape reconstruction method and successively refine its results, while generating the per-pixel diffuse and specular components that are required for realistic rendering. As we demonstrate in a series of qualitative and quantitative experiments, AvatarMe outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image that, for the first time, bridges the uncanny valley.
