Editing Conditional Radiance Fields (2105.06466v2)

Abstract: A neural radiance field (NeRF) is a scene model supporting high-quality view synthesis, optimized per scene. In this paper, we explore enabling user editing of a category-level NeRF - also known as a conditional radiance field - trained on a shape category. Specifically, we introduce a method for propagating coarse 2D user scribbles to the 3D space, to modify the color or shape of a local region. First, we propose a conditional radiance field that incorporates new modular network components, including a shape branch that is shared across object instances. Observing multiple instances of the same category, our model learns underlying part semantics without any supervision, thereby allowing the propagation of coarse 2D user scribbles to the entire 3D region (e.g., chair seat). Next, we propose a hybrid network update strategy that targets specific network components, which balances efficiency and accuracy. During user interaction, we formulate an optimization problem that both satisfies the user's constraints and preserves the original object structure. We demonstrate our approach on various editing tasks over three shape datasets and show that it outperforms prior neural editing approaches. Finally, we edit the appearance and shape of a real photograph and show that the edit propagates to extrapolated novel views.

• The paper presents a novel contribution by developing an interactive system that enables intuitive edits on 3D models using conditional radiance fields.
• The methodology employs a modular neural network architecture with shared and instance-specific shape networks to effectively disentangle shape and color for targeted edits.
• The approach demonstrates superior performance over traditional NeRF and GAN-based methods, with improvements validated by metrics such as PSNR, SSIM, and LPIPS across diverse datasets.

Insights into "Editing Conditional Radiance Fields"

The paper "Editing Conditional Radiance Fields" presents a novel approach to editing 3D object representations that leverages the strengths of neural radiance fields (NeRF). The primary contribution lies in the development of an interactive system that enables users to perform intuitive edits on 3D models represented by conditional radiance fields. The conditional radiance field expands upon the traditional NeRF by incorporating latent vectors that represent shape and appearance, specifically trained over an entire class of objects. This setup allows for effective propagation of sparse user input across the 3D structure and maintains consistency across different viewpoints.

Methodology and Architecture

The proposed system introduces a modular neural network architecture composed of a shared shape network and an instance-specific shape network that collaboratively learn a prior over object classes. This design choice provides a strong inductive bias that aids in the disentanglement of shape and color. Key to the system's effectiveness is the strategy of employing different network components to achieve specific types of edits. For instance, color edits target later network layers to focus on appearance, whereas shape edits may adjust parameters associated with structure, ensuring minimal disruption to other features of the model.

Numerical Results and Comparisons

The authors benchmark their approach across multiple datasets, notably the PhotoShape, Aubry chairs, and CARLA datasets, showcasing their model's superiority in rendering realism and editing quality compared to existing methods. They provide quantitative results using metrics such as PSNR, SSIM, and LPIPS, demonstrating significant improvements over baseline methods, including single-instance NeRF and GAN-based editing techniques. The paper reports better consistency in both shape and color through their method, even outperforming models when trained separately on single instances.

Practical Implications

The methodology described in this paper has significant implications for 3D content creation, particularly for applications in visual effects and augmented reality. The ability to make precise, local edits to 3D models without explicit, manual intervention allows for a more efficient workflow. This functionality is vital for creative industries where rapid prototyping and iterations are necessary. Furthermore, the disentangled representation of shape and color paves the way for sophisticated, automated editing tasks without requiring detailed user inputs.

Future Directions

This research opens up several avenues for further exploration. One potential area is improving the interactivity of shape editing processes, as current methods can be time-intensive due to rendering requirements. Advances in real-time NeRF rendering could greatly enhance user experience in practical applications. Additionally, expanding the system to accommodate more complex scene topologies and lighting conditions remains an open challenge that could increase the method's applicability across diverse contexts.

In summary, the paper establishes a compelling framework for editing conditional radiance fields, offering substantial improvements in user control over 3D editing tasks. The approach aligns well with the evolving demands in creative fields, and its underlying principles may apply to adjacent areas such as 3D animation and design.