3D-aware Blending with Generative NeRFs (2302.06608v3)

Abstract: Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3D camera poses and object shapes. To tackle these issues, we propose a 3D-aware blending method using generative Neural Radiance Fields (NeRF), including two key components: 3D-aware alignment and 3D-aware blending. For 3D-aware alignment, we first estimate the camera pose of the reference image with respect to generative NeRFs and then perform 3D local alignment for each part. To further leverage 3D information of the generative NeRF, we propose 3D-aware blending that directly blends images on the NeRF's latent representation space, rather than raw pixel space. Collectively, our method outperforms existing 2D baselines, as validated by extensive quantitative and qualitative evaluations with FFHQ and AFHQ-Cat.

• The paper introduces a 3D-aware blending approach that leverages Generative NeRFs to overcome the limitations of traditional 2D blending techniques.
• It employs CNN-based pose estimation, ICP for local alignment, and Poisson blending to optimize image synthesis and detail preservation.
• The method demonstrates superior performance on key metrics and offers promising applications in image editing, VR, and AR technologies.

3D-aware Blending with Generative NeRFs

The paper presents an innovative approach to image blending, leveraging 3D-aware methods through Generative Neural Radiance Fields (NeRF). Traditional blending techniques typically rely on 2D methods, which may struggle with alignment and realism, particularly when images differ in 3D poses or object shapes. This research addresses these limitations by introducing two core components: 3D-aware alignment and 3D-aware blending, resulting in improved image synthesis that respects both the geometry and appearance of the reference images.

Summary of the Core Method

The contribution centers around using generative NeRFs to manage image alignment and blending challenges. Key steps involve:

1. 3D-aware Alignment: The alignment process begins with estimating the camera poses of two given images to adjust their orientation. This is achieved through a CNN encoder trained on pose estimation, followed by Pivotal Tuning Inversion to determine latent codes for precise alignment. The proposed method also incorporates local alignment using the ICP algorithm to handle scale and translation differences in target regions effectively.
2. 3D-aware Blending: The blending strategy extends beyond mere color matching by incorporating volume density from NeRF. This involves optimizing a latent code using a combination of image-blending and density-blending losses, allowing for blending operations that respect both the original and reference images’ attributes in a coherent manner.
3. Integration with Poisson Blending: For enhanced detail preservation in regions far from the blending target, the authors combine their method with Poisson blending, which helps maintain background integrity without compromising the blending quality.

Numerical Results and Claims

The robustness of the proposed methodology is exhibited through extensive quantitative and qualitative evaluations. The method demonstrates superior performance compared to established 2D-oriented baselines across several metrics, including masked LPIPS and Kernel Inception Distance (KID). Additionally, user studies reveal a preference for the proposed method over competing techniques in perceptual realism. The paper asserts that this improved performance arises because of the effective alignment and blending strategies that account for 3D structural information.

Implications and Future Directions

The implications of this work are significant for content creation applications involving image editing, where high-quality blending is required. The ability to blend images while maintaining realism and consistency in optical quality may benefit various domains, including virtual and augmented reality. The theoretical developments emphasize the importance of incorporating 3D-awareness into image synthesis tasks, suggesting new directions for research into alignment and blending algorithms.

Future developments could extend the proposed method to real-time applications by optimizing efficiency, potentially through pretrained models rather than iterative GAN inversions. As GPU capabilities advance, leveraging real-time performance would make such methods viable in interactive user interfaces for photo editing and related fields.

The method also opens pathways for advancing 3D model training algorithms, where understanding and managing 3D attributes become an integral part. Moving beyond NeRFs, exploring integration with other 3D-aware generative architectures, such as SDF-based models, could further enhance the flexibility and application range of this technology.