StyleGAN knows Normal, Depth, Albedo, and More (2306.00987v1)

Abstract: 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.

• The paper reveals that latent space offsets in StyleGAN enable intrinsic image generation without extra training.
• It demonstrates that generated intrinsics, including normals, depth maps, albedo, and shading, outperform state-of-the-art regression methods.
• The findings open new avenues in computer vision, promoting generative models as robust, versatile intrinsic image predictors.

StyleGAN Knows Normal, Depth, Albedo, and More: An Overview

The paper, "StyleGAN knows Normal, Depth, Albedo, and More," presents a paper on the intrinsic capacity of StyleGAN to generate high-quality intrinsic images like surface normals, depth maps, albedo, and shading, without requiring explicit training on such inputs. This research highlights the generative prowess of StyleGAN, a well-established network recognized for its ability to produce visually appealing images.

Methodology and Findings

The authors illustrate that intrinsic image generation by StyleGAN arises from latent space manipulations, specifically through fixed offsets for each intrinsic type. This method capitalizes on the latent space of a pre-trained StyleGAN without additional learning or fine-tuning. The paper argues that the success of this approach is not attributed to accidental properties from training StyleGAN but rather due to innate representational capabilities intrinsic to the model.

The evaluation showcased the efficacy of StyleGAN-generated intrinsic images compared to state-of-the-art (SOTA) regression methods. For example, in depth estimation, StyleGAN outperformed existing techniques, exhibiting both qualitative and quantitative rigor. Furthermore, the generated intrinsics demonstrated robustness to relighting effects, a significant advantage over current SOTA methods which are often sensitive to such changes.

Implications and Future Directions

This work opens several avenues for leveraging generative models like StyleGAN in tasks traditionally solved by regression approaches. The paper hints at potential applications within computer vision, especially for scenarios where robustness against environmental changes is crucial.

Moreover, this finding suggests a promising area of research involving other generative models and their intrinsic image capabilities. Future efforts could focus on improving GAN inversion techniques to allow real-world applications where latent manipulations align seamlessly with real image spaces. Additionally, if generative models consistently reveal intrinsic images, it underscores their representation efficiency—providing impetus for both theoretical exploration and practical innovations.

Conclusion

This research underscores that StyleGAN's ability to generate intrinsic images is not merely a byproduct of its generative prowess but suggests a deeper structural understanding encoded within its latent space. As advancements in GAN inversion continue, the prospect of turning generative models into robust intrinsic image predictors becomes increasingly plausible. The paper contributes significantly to our understanding of generative models and their potential expansions into new application domains.