We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and illuminance. SfSNet is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real world images. This allows the network to capture low frequency variations from synthetic and high frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation.
The problem of decomposing shape, reflectance and illuminance from a single image is a classical problem in computer vision and has been studied in various forms such as intrinsic image decomposition @cite_8 and Shape from Shading (SfS) @cite_30 @cite_26 . Recent work of SIRFS @cite_34 performs decomposition of an object into surface normal, albedo and lighting assuming lambertian reflection by formulating extensive priors in an optimization framework. The problem of inverse rendering in the form of SfS gained particular attention in the domain of human facial modeling. This research was precipitated by the advent of the 3D Morphable Model (3DMM) @cite_22 as a potential prior for shape and reflectance. Recent works used facial priors to reconstruct shape from a single image @cite_27 @cite_3 @cite_5 @cite_13 or from multiple images @cite_15 . Classical SfS methods fail to produce realistic decomposition on unconstrained real images. More recently, Saito al proposes a method to synthesize a photorealistic albedo from a partial albedo obtained by traditional methods @cite_29 .
@cite_30 @cite_26 @cite_22 @cite_8 @cite_29 @cite_3 @cite_27 @cite_5 @cite_15 @cite_34 @cite_13
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We address the problem of recovering shape, albedo, and illumination from a single grayscale image of an object, using shading as our primary cue. Because this problem is fundamentally underconstrained, we construct statistical models of albedo and shape, and define an optimization problem that searches for the most likely explanation of a single image. We present two priors on albedo which encourage local smoothness and global sparsity, and three priors on shape which encourage flatness, outward-facing orientation at the occluding contour, and local smoothness. We present an optimization technique for using these priors to recover shape, albedo, and a spherical harmonic model of illumination. Our model, which we call SAIFS (shape, albedo, and illumination from shading) produces reasonable results on arbitrary grayscale images taken in the real world, and outperforms all previous grayscale “intrinsic image” — style algorithms on the MIT Intrinsic Images dataset. We relax the long-held and problematic assumption in shape-from-shading (SFS) that albedo must be uniform or known, and address the problem of “shape and albedo from shading” (SAFS). Using models normally reserved for natural image statistics, we impose “naturalness” priors over the albedo and shape of a scene, which allows us to simultaneously recover the most likely albedo and shape that explain a single image. A simplification of our algorithm solves classic SFS, and our SAFS algorithm can solve the intrinsic image decomposition problem, as it solves a superset of that problem. We present results for SAFS, SFS, and intrinsic image decomposition on real lunar imagery from the Apollo missions, on our own pseudo-synthetic lunar dataset, and on a subset of the MIT Intrinsic Images dataset[15]. Our one unified technique appears to outperform the previous best individual algorithms for all three tasks. Our technique allows a coarse observation of shape (from a laser rangefinder or a stereo algorithm, etc) to be incorporated a priori. We demonstrate that even a small amount of low-frequency information dramatically improves performance, and motivate the usage of shading for high-frequency shape (and albedo) recovery. To deal with image variations due to illumination problem, recently Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them had the assumption that the 3D surface normal and albedo are known. This assumption limits this algorithm's application. In this paper, we present a novel method for modeling 3D face shape and albedo from only three images with unknown light directions and this work well fills the blank, which Ramamoorthi and Basri left. By taking the advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction. We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variations observed over time to infer constraints on the reflectance and solve the ill-posed image decomposition problem. In particular, we derive an adaptive local energy from the observations of each local neighborhood over time, and integrate distant pairwise constraints to enforce coherent decomposition across all surfaces with consistent shading changes. Our method is solely based on multiple observations of a Lambertian scene under varying illumination and does not require user interaction, scene geometry, or an explicit lighting model. We compare our results with several intrinsic decomposition methods on a number of synthetic and captured datasets.