AvatarMe: Realistically Renderable 3D Facial Reconstruction “in-the-wild” Alexandros Lattas1,2 Stylianos Moschoglou1,2 Baris Gecer1,2 Stylianos Ploumpis1,2 Vasileios Triantafyllou2 Abhijeet Ghosh1 Stefanos Zafeiriou1,2 1Imperial College London, UK 2FaceSoft.io 1 2 {a.lattas,s.moschoglou,b.gecer,s.ploumpis,ghosh,s.zafeiriou}@imperial.ac.uk
[email protected] Figure 1: From left to right: Input image; Predicted reflectance (diffuse albedo, diffuse normals, specular albedo and specular normals); Rendered reconstruction in different environments, with detailed reflections; Rendered result with head completion. Abstract 1. Introduction Over the last years, with the advent of Generative Ad- The reconstruction of a 3D face geometry and texture is versarial Networks (GANs), many face analysis tasks have one of the most popular and well-studied fields in the in- accomplished astounding performance, with applications tersection of computer vision, graphics and machine learn- including, but not limited to, face generation and 3D face ing. Apart from its countless applications, it demonstrates reconstruction from a single “in-the-wild” image. Never- the power of recent developments in scanning, learning and theless, to the best of our knowledge, there is no method synthesizing 3D objects [3, 44]. Recently, mainly due to which can produce high-resolution photorealistic 3D faces the advent of deep learning, tremendous progress has been from “in-the-wild” images and this can be attributed to the: made in the reconstruction of a smooth 3D face geometry, (a) scarcity of available data for training, and (b) lack of ro- even from images captured in arbitrary recording conditions bust methodologies that can successfully be applied on very (also referred to as “in-the-wild”) [13, 14, 33, 36, 37].