NeRF Editing and Inpainting Techniques: Conclusion and References

Table of Links

Abstract and 1. Introduction

2. Related Work

2.1. NeRF Editing and 2.2. Inpainting Techniques

2.3. Text-Guided Visual Content Generation

3. Method

3.1. Training View Pre-processing

3.2. Progressive Training

3.3. 4D Extension

4. Experiments and 4.1. Experimental Setups

4.2. Ablation and comparison

5. Conclusion and 6. References

5. Conclusion

We introduce Inpaint4DNeRF, a unified framework that can directly generate text-guided, background-appropriate, and multi-view consistent content within an existing NeRF. To ensure convergence from the original object to a completely different object, we propose a training image pre-processing method that projects from initially inpainted seed images to other views, with details refined by stable diffusion. A

roughly multiview consistent set of training images, combined with depth regularization, guarantees coarse convergence on geometry and appearance. Finally, the coarse NeRF is fine-tuned by iterative dataset update with stable diffusion. Our baseline can be readily extended to dynamic NeRF inpainting by generalizing the seed-image-to-other strategy from the spatial domain to the temporal domain. We provide

3D and 4D examples to demonstrate the effectiveness of our method. We also investigate the role of various elements in our baseline by ablation and comparison.

The proposed framework expands the possibilities for realistic and coherent scene editing in 3D and 4D settings. However, our current baseline still has some limitations, providing room for further improvement. Specifically, it is challenging for our method to handle complex geometry generation with a camera set covering wide angles. The consistency of the final NeRF can still be improved. In addition, to extend our method fully into 4D, certain techniques are required to further improve temporal consistency and maintain better multiview consistency across frames. We hope that our proposed baseline can inspire these future research directions for text-guided generative NeRF inpainting.

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