[Paper Review] Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields

Xiangyu Wang, Jingsen Zhu+5 more|arXiv (Cornell University)|Jul 27, 2023

3D Shape Modeling and Analysis4 citations

TL;DR

Seal-3D introduces an interactive, pixel-level editing system for Neural Radiance Fields (NeRF) that enables instant preview (≈1 second) and high-fidelity editing through a two-stage teacher-student distillation framework with a proxy function mapping user edits to the original NeRF space. The method supports diverse editing types—such as brushing, bounding box manipulation, and color editing—while preserving shading and geometric consistency without requiring explicit meshes or complex proxies.

ABSTRACT

With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation. While previous works have explored NeRF editing from various perspectives, they are restricted in editing flexibility, quality, and speed, failing to offer direct editing response and instant preview. The key challenge is to conceive a locally editable neural representation that can directly reflect the editing instructions and update instantly. To bridge the gap, we propose a new interactive editing method and system for implicit representations, called Seal-3D, which allows users to edit NeRF models in a pixel-level and free manner with a wide range of NeRF-like backbone and preview the editing effects instantly. To achieve the effects, the challenges are addressed by our proposed proxy function mapping the editing instructions to the original space of NeRF models in the teacher model and a two-stage training strategy for the student model with local pretraining and global finetuning. A NeRF editing system is built to showcase various editing types. Our system can achieve compelling editing effects with an interactive speed of about 1 second.

Motivation & Objective

• To address the lack of interactive, fine-grained editing tools for implicit 3D representations like NeRF.
• To overcome the challenges of local editing in non-local implicit networks, where parameter updates cause global distortions.
• To enable direct, user-friendly editing with instant visual feedback, eliminating reliance on intermediate proxies like meshes.
• To preserve view-consistent shading and geometric details during editing, especially in complex surface effects like shadows and bumps.
• To support a wide range of editing operations—including geometry deformation, color painting, and object transfer—within a unified, interactive system.

Proposed method

• A proxy function maps user-edit instructions (e.g., brush strokes or bounding boxes) from the editing space to the original NeRF’s 3D scene space, establishing correspondence between edits and underlying neural fields.
• A two-stage training strategy is employed: first, local pretraining with fixed MLPs and updated positional embedding grids to generate coarse, instant previews; second, global finetuning to refine results.
• Teacher-student distillation is used, where the teacher model provides supervision via the proxy function to guide the student model’s parameter updates with content-aware loss.
• Local-aware positional embedding grids are introduced to enable localized editing effects without global network disruption.
• The student model is trained with a combination of photometric and local geometry losses to ensure view consistency and accurate surface detail reproduction.
• The system integrates multiple editing tools (bounding box, brushing, anchor, color) into a GUI-based interface, enabling real-time interaction and preview.

Experimental results

Research questions

• RQ1 Can a NeRF editing system achieve interactive, pixel-level editing with sub-second preview latency while preserving geometric and shading fidelity?
• RQ2 How can a proxy function effectively map user-edit instructions to the implicit 3D space of a NeRF without relying on explicit 3D geometry like meshes?
• RQ3 What training strategy enables both fast convergence for instant preview and high-quality results through a balance of local and global optimization?
• RQ4 To what extent can local editing be achieved in a non-local implicit representation without causing global distortions or artifacts?
• RQ5 Can the method support diverse editing types—such as geometry deformation, color editing, and object transfer—simultaneously and view-consistently?

Key findings

• Seal-3D achieves interactive editing with a preview time of approximately 1 second, significantly faster than existing methods that require minutes or hours for convergence.
• The two-stage training strategy (pretraining + finetuning) enables instant coarse results while allowing further refinement in under 2 minutes for high-quality outputs.
• The method preserves complex shading effects such as shadows on lifted surfaces and bumps, outperforming baselines like NeuMesh and Liu et al. [20] in visual realism and artifact reduction.
• Results from the student network often surpass the teacher model, especially in reducing view-inconsistent artifacts such as floating objects, demonstrating effective distillation.
• The system supports diverse editing operations—including object transfer, geometry deformation, and texture painting—without requiring explicit mesh proxies, enhancing user-friendliness and interactivity.
• Abllation studies confirm that fixing MLPs during pretraining prevents global degradation and that the two-stage strategy effectively balances local fitting and global consistency.