Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs (2011.00844v4)

Abstract: Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image manifold, it is unclear whether they implicitly capture the underlying 3D object structures. And if so, how could we exploit such knowledge to recover the 3D shapes of objects in the images? To answer these questions, in this work, we present the first attempt to directly mine 3D geometric cues from an off-the-shelf 2D GAN that is trained on RGB images only. Through our investigation, we found that such a pre-trained GAN indeed contains rich 3D knowledge and thus can be used to recover 3D shape from a single 2D image in an unsupervised manner. The core of our framework is an iterative strategy that explores and exploits diverse viewpoint and lighting variations in the GAN image manifold. The framework does not require 2D keypoint or 3D annotations, or strong assumptions on object shapes (e.g. shapes are symmetric), yet it successfully recovers 3D shapes with high precision for human faces, cats, cars, and buildings. The recovered 3D shapes immediately allow high-quality image editing like relighting and object rotation. We quantitatively demonstrate the effectiveness of our approach compared to previous methods in both 3D shape reconstruction and face rotation. Our code is available at https://github.com/XingangPan/GAN2Shape.

• The paper demonstrates that 2D GANs embed implicit 3D information, which can be exploited via an iterative projection scheme.
• The paper introduces an unsupervised framework using a weak convex shape prior to generate pseudo samples and explore latent space directions.
• The paper achieves competitive depth and angle estimation metrics without symmetry constraints, setting new benchmarks in 3D reconstruction.

An Expert Overview of "Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs"

This paper explores a novel method of unsupervised 3D shape reconstruction leveraging pre-trained 2D Generative Adversarial Networks (GANs). Traditionally, 2D GANs are recognized for their effectiveness in synthesizing high-fidelity 2D images, but questions remain regarding their capacity to implicitly encode 3D geometric information. This paper investigates whether these networks contain embedded 3D knowledge and, if so, how it can be extracted for reconstructing 3D shapes.

Methodology

The paper presents an innovative framework that utilizes off-the-shelf 2D GANs to infer 3D shapes from single 2D images. The core of this approach is an iterative scheme that exploits the image manifold captured by the GANs. This scheme explores and exploits variations in viewpoints and lighting conditions without requiring 2D keypoint or 3D annotations.

A weak convex shape prior, such as an ellipsoid, is used to establish a baseline for rendering multiple pseudo samples with varied viewpoints and lighting conditions. These samples guide the discovery of semantic directions in the GAN's latent space that correspond to different perspectives and lighting. By reconstructing these pseudo samples using the GAN, natural photographic variations are generated, termed as projected samples. These serve as reference points for refining the initial 3D shape.

Numerical Results

The paper provides quantitative evaluations on benchmarks such as BFM, revealing competitive performance compared to existing state-of-the-art unsupervised methods in 3D reconstruction. Statistical metrics such as scale-invariant depth error (SIDE) and mean angle deviation (MAD) indicate superior precision in depth and angle estimation, with the method outperforming recent baselines. The research notably achieves enhanced accuracy without relying on symmetry assumptions commonly invoked by other methods.

Implications and Future Directions

This research contributes to the field by demonstrating that 2D GANs can be harnessed for 3D shape learning, effectively bridging 2D and 3D graphics. The implications of this work are significant, particularly in applications requiring realistic 3D-aware manipulations such as object rotation and relighting, without the need for external 3D models.

The insights gleaned from this paper could inform future advancements in computer vision and graphics, especially in developing more efficient frameworks for 3D shape generation. Potential areas for future exploration include enhancing the method's applicability to more complex shapes and improving the parameterization of 3D meshes to capture back-side object details.

Overall, this paper enriches the discourse on leveraging pre-trained 2D models for advanced tasks, offering a fresh perspective on the latent potential of existing AI models in capturing multidimensional spatial properties.