Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories (2412.05279v2)

Abstract: Recent advancements in text-based diffusion models have accelerated progress in 3D reconstruction and text-based 3D editing. Although existing 3D editing methods excel at modifying color, texture, and style, they struggle with extensive geometric or appearance changes, thus limiting their applications. To this end, we propose Perturb-and-Revise, which makes possible a variety of NeRF editing. First, we perturb the NeRF parameters with random initializations to create a versatile initialization. The level of perturbation is determined automatically through analysis of the local loss landscape. Then, we revise the edited NeRF via generative trajectories. Combined with the generative process, we impose identity-preserving gradients to refine the edited NeRF. Extensive experiments demonstrate that Perturb-and-Revise facilitates flexible, effective, and consistent editing of color, appearance, and geometry in 3D. For 360{\deg} results, please visit our project page: https://susunghong.github.io/Perturb-and-Revise.

• The paper introduces a novel Perturb-and-Revise method that uses parameter perturbation and generative trajectories for flexible 3D editing.
• It combines score distillation with parameter interpolation to navigate local minima and preserve object identity during major modifications.
• Experiments on Objaverse and synthetic datasets demonstrate state-of-the-art performance in editing color, shape, and pose for diverse applications.

Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories

The paper "Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories" presents an innovative framework for 3D object editing, specifically addressing some of the limitations in current methodologies that rely on Neural Radiance Fields (NeRFs). The authors have introduced a method, termed Perturb-and-Revise (PnR), which leverages parameter perturbation in the NeRF optimization process to facilitate flexible and natural 3D editing based on text prompts. This research makes significant strides in overcoming the challenges posed by significant geometric and appearance changes during 3D object modification.

Overview of Methodology

The Perturb-and-Revise approach exploits the synergistic potential between score distillation and parameter perturbation within NeRFs, offering a novel paradigm for object editing. The core strategy involves a two-step procedure:

1. Parameter Perturbation: The methodology begins with perturbing the NeRF parameters. By introducing randomness in the initialization through parameter interpolation, the model escapes local minima, which traditionally limits editing capabilities. This is akin to a form of gradient-based optimization, whereby perturbations are methodically applied to nudge the model parameters towards regions that are likely more amenable to edits dictated by natural language prompts.
2. Generative Trajectories and Editing: With the parameters appropriately perturbed, the subsequent editing process involves navigating the parameter space through generative trajectories informed by text prompts. The framework employs an identity-preserving gradient (IPG) to ensure that major characteristics of the original object remain faithful while integrating the edits.

A significant contribution of this paper is the adaptive selection of the perturbation parameter, η, which is critical to achieving a balance between preserving the original NeRF's attributes and allowing significant edits such as pose changes or the introduction of new components.

Results and Implications

The results, as evaluated on various datasets including Objaverse and synthetic 3D fashion objects, indicate that the PnR framework achieves state-of-the-art results across a range of editing tasks. Notably, the framework exhibited superior flexibility in implementing color, pattern, shape, pose, and object changes, outperforming existing techniques such as Instruct-NeRF2NeRF and Posterior Distillation Sampling.

The implications of this research extend to various practical fields, including animation, design, and virtual reality, where the ability to intuitively and effectively edit 3D models is highly coveted. The Perturb-and-Revise method promises increased efficiency and accessibility, as it does not require intensive retraining on large datasets and can operate effectively with fewer iterations compared to traditional approaches.

Future Directions

While the paper demonstrates significant advancements, there are inherent limitations that future research could explore, such as extending the framework to handle dynamic scenes and video editing. The current reliance on diffusion models, which may introduce biases, also suggests room for improvement in model robustness and the provision of bias-free editing capabilities. Future work could also focus on refining the parameter perturbation techniques or integrating multi-modal inputs to further enhance the versatility and utility of the PnR framework.

Conclusion

The Perturb-and-Revise method represents an important advancement in the domain of 3D object editing, offering a flexible, efficient, and intuitive tool that broadens the scope of text-based NeRF editing. By addressing the challenges associated with significant geometrical modifications and optimizing the parameter space for coherent updates, this framework sets the stage for more adaptable and comprehensive editing solutions in both static and potentially dynamic 3D scenes.