3D GAN Inversion with Pose Optimization (2210.07301v2)

Abstract: With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image. However, the explicit viewpoint control acts as a main hindrance in the 3D GAN inversion process, as both camera pose and latent code have to be optimized simultaneously to reconstruct the given image. Most works that explore the latent space of the 3D-aware GANs rely on ground-truth camera viewpoint or deformable 3D model, thus limiting their applicability. In this work, we introduce a generalizable 3D GAN inversion method that infers camera viewpoint and latent code simultaneously to enable multi-view consistent semantic image editing. The key to our approach is to leverage pre-trained estimators for better initialization and utilize the pixel-wise depth calculated from NeRF parameters to better reconstruct the given image. We conduct extensive experiments on image reconstruction and editing both quantitatively and qualitatively, and further compare our results with 2D GAN-based editing to demonstrate the advantages of utilizing the latent space of 3D GANs. Additional results and visualizations are available at https://3dgan-inversion.github.io .

• The paper proposes a hybrid initialization that jointly refines latent codes and camera poses to overcome local minima and ensure multi-view consistency.
• It employs a pixel-wise depth optimization using NeRF parameters to enforce geometrical accuracy and reduce artifacts during 3D image reconstruction.
• Regularization and pivotal tuning techniques significantly boost reconstruction fidelity and editability, outperforming previous approaches on key benchmarks.

An Examination of 3D GAN Inversion with Pose Optimization

The paper "3D GAN Inversion with Pose Optimization" addresses the challenge of projecting 2D images into 3D-aware Generative Adversarial Networks (GANs) using an innovative approach that combines image inversion and pose optimization. This technique unlocks a more meaningful manipulation and representation of images by maintaining multi-view consistency and enhancing 3D reconstruction, essential for applications demanding a coherent 3D shape understanding.

Core Contributions

The authors propose a method for 3D GAN inversion that simultaneously optimizes the latent code and the camera pose, overcoming significant obstacles compared to traditional 2D GAN inversion. Here are the main elements of their contribution:

1. Hybrid Initialization Approach: The paper leverages a hybrid approach that first employs an encoder to provide a rough estimation of latent codes and camera poses. This initialization significantly reduces the risk of falling into local minima during optimization, which is a common challenge in projecting images onto the latent space of 3D GANs.
2. Depth-Based Optimization: They introduce a pixel-wise depth calculation mechanism using NeRF parameters. This not only facilitates accurate image reconstruction but also contributes to geometrical consistency across different views by enforcing a depth-based warping loss.
3. Regularization Techniques: The paper incorporates depth smoothness regularization and noise regularization to avert defects known in 3D neural rendering such as floating artifacts, ensuring that the 3D reconstructions remain robust across varying conditions.
4. Pivotal Tuning: Extending previous techniques in 2D GANs, pivotal tuning allows for slight adjustments in the GAN’s generator manifold, offering improvements in both reconstruction fidelity and the editability of the inverted images.

Results and Implications

The findings demonstrate superior performance over existing methods across several benchmarks such as Multi-Scale Structural Similarity (MS-SSIM), LPIPS for perceptual differences, and identity similarity assessments. These experiments validate the approach's effectiveness in creating high-quality reconstructions and enable meaningful semantic edits using GANSpace, thereby broadening the utility of 3D GANs in real-world applications.

Although primarily focused on human face datasets as demonstrated with FFHQ and CelebA-HQ datasets, the method also shows potential across other domains like animal faces. This reveals the versatility of the approach for a wide range of applications requiring 3D reconstructions and view-consistent editing.

Future Developments

The paper opens avenues for further exploration in AI and computer vision. Enhancements could include integrating advanced neural rendering techniques or extending to other domains like articulated objects or environments. Further research could also explore extending the inversion capabilities in conjunction with other generative models for broader applications in virtual reality, augmented reality, or gaming.

Conclusion

The paper's approach to embedding 2D images within the manifolds of 3D GANs, while addressing pose optimization, represents a significant step in computer vision. By facilitating reliable, multi-view consistent edits and reconstructions, this method contributes to the evolving landscape of automated 3D content creation and manipulation, offering many promising implications for future AI applications.