Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation (2202.13162v1)

Abstract: We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Our method is based on $\pi$-GAN, a generative model for unconditional 3D-aware image synthesis, which maps random latent codes to radiance fields of a class of objects. We jointly optimize (1) the $\pi$-GAN objective to utilize its high-fidelity 3D-aware generation and (2) a carefully designed reconstruction objective. The latter includes an encoder coupled with $\pi$-GAN generator to form an auto-encoder. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. Applications of our pipeline include 3d avatar generation, object-centric novel view synthesis with a single input image, and 3d-aware super-resolution, to name a few.

• The paper introduces Pix2NeRF, which uses an unsupervised conditional π-GAN to convert a single image into 3D neural radiance fields by disentangling content and pose.
• It integrates GAN training, latent space consistency, and reconstruction objectives to achieve photorealistic novel view synthesis.
• Its innovation paves the way for few-shot and unsupervised 3D reconstruction, enhancing flexibility in neural rendering applications.

Pix2NeRF: Unsupervised Conditional $\pi$-GAN for Single Image to Neural Radiance Fields Translation

The paper introduces Pix2NeRF, an innovative approach designed to enable the generation of Neural Radiance Fields (NeRF) from a single input image without requiring multi-view, 3D, or pose supervision. The method bridges generative latent space modeling via GANs with the fidelity of NeRF-based scene representations. Built on the $\pi$-GAN architecture, Pix2NeRF contributes to the field by combining a few key objectives: unsupervised generative modeling, conditional GAN-based NeRF inversion, and the disentangle-ment of content and pose in the latent space for robust neural rendering.

Technical Contributions and Methodology

Pix2NeRF extends the capability of the $\pi$-GAN by introducing an encoder that transforms input images into a latent representation comprising content and pose codes. This enables single-shot inference of 3D-aware neural representations. The method involves several concurrent training objectives:

1. GAN Training and Adversarial Learning: Ensuring that the NeRF generated outputs can be mistaken for genuine samples by a discriminator, with supervision relying solely on photorealistic fidelity.
2. Latent Space Consistency (GAN Inversion): The encoder is optimized to provide a latent space mapping coherent with the generator's structure, which means consistent encoding of content and pose from various views.
3. Reconstruction Objective: By mapping a real image into the latent space and then reconstructing the image via latent code and predicted pose, reconstruction loss encourages visual consistency.
4. Conditional Adversarial Training: This bridges the conditional generation of novel views with unsupervised data, enhancing the quality and variability of generated views.
5. Warm-Up Strategy: A critical element in training involves a warm-up period for the encoder, allowing the generator to grasp rough data alignment, reducing overfitting risks and enabling smoother domain transitions.

Implications and Future Directions

Pix2NeRF offers substantial advancements toward few-shot and unsupervised machine vision tasks, particularly in single-image 3D reconstruction. The method effectively decouples view-dependent scene details via adversarial networks integrated with neural radiance field rendering, allowing for applications in novel view synthesis, 3D content generation, and more. Future work could explore extending the architecture to handle category-agnostic scenes, improving scalability toward more complex environments, and integrating with advanced feature extractors for enhanced detail retention.

Potential avenues for enhancement include leveraging more sophisticated encoder architectures that benefit from recent developments in GAN feed-forward inversion techniques or exploring additional conditioning variables to refine the generative process further. The evolution of Pix2NeRF could see integration with pixel-aligned feature methods for broader applicability across diverse datasets and object classes, and utilizing models like EG3D for improved visual fidelity in generation tasks.

Conclusion

The significance of Pix2NeRF lies in its ability to generate 3D representations from single images, a task previously difficult without extensive datasets and deep multi-view data. By harnessing the capabilities of $\pi$-GAN in tandem with novel training frameworks, this approach demonstrates the potential of unsupervised learning paradigms in the synthesis and interpolation of neural scene representations, marking a step forward in how AI interprets and reconstructs our visual world.