This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images.

Intrinsic images, in the original sense, are image-like maps of scene properties like depth, normal, albedo, or shading. This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images.

Neural Information Processing Systems http://nips.cc/

This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images.

Intrinsic images, in the original sense, are image-like maps of scene properties like depth, normal, albedo, or shading. This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images. The StyleGAN we used was pretrained by others, so this property is not some accident of our training regime. We show that there are image transformations StyleGAN will {\em not} produce in this fashion, so StyleGAN is not a generic image regression engine. It is conceptually exciting that an image generator should know'' and represent intrinsic images.

Intrinsic images, in the original sense, are image-like maps of scene properties like depth, normal, albedo, or shading. This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images. The StyleGAN we used was pretrained by others, so this property is not some accident of our training regime. We show that there are image transformations StyleGAN will {\em not} produce in this fashion, so StyleGAN is not a generic image regression engine. It is conceptually exciting that an image generator should know'' and represent intrinsic images.

Neural Information Processing Systems http://nips.cc/

We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors.

Many methods have been proposed to solve the problems of recovering intrinsic scene properties such as shape, reflectance and illumination from a single image, and object class segmentation separately. While these two problems are mutually informative, in the past not many papers have addressed this topic. In this work we explore such joint estimation of intrinsic scene properties recovered from an image, together with the estimation of the objects and attributes present in the scene. In this way, our unified framework is able to capture the correlations between intrinsic properties (reflectance, shape, illumination), objects (table, tv-monitor), and materials (wooden, plastic) in a given scene. For example, our model is able to enforce the condition that if a set of pixels take same object label, e.g.

Intrinsic images, in the original sense, are image-like maps of scene properties like depth, normal, albedo or shading. This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images. The procedure is straightforward. We show that, if StyleGAN produces $G({w})$ from latents ${w}$, then for each type of intrinsic image, there is a fixed offset ${d}_c$ so that $G({w}+{d}_c)$ is that type of intrinsic image for $G({w})$. Here ${d}_c$ is {\em independent of ${w}$}. The StyleGAN we used was pretrained by others, so this property is not some accident of our training regime. We show that there are image transformations StyleGAN will {\em not} produce in this fashion, so StyleGAN is not a generic image regression engine. It is conceptually exciting that an image generator should ``know'' and represent intrinsic images. There may also be practical advantages to using a generative model to produce intrinsic images. The intrinsic images obtained from StyleGAN compare well both qualitatively and quantitatively with those obtained by using SOTA image regression techniques; but StyleGAN's intrinsic images are robust to relighting effects, unlike SOTA methods.