3D Gaussian Editing with A Single Image (2408.07540v1)

Abstract: The modeling and manipulation of 3D scenes captured from the real world are pivotal in various applications, attracting growing research interest. While previous works on editing have achieved interesting results through manipulating 3D meshes, they often require accurately reconstructed meshes to perform editing, which limits their application in 3D content generation. To address this gap, we introduce a novel single-image-driven 3D scene editing approach based on 3D Gaussian Splatting, enabling intuitive manipulation via directly editing the content on a 2D image plane. Our method learns to optimize the 3D Gaussians to align with an edited version of the image rendered from a user-specified viewpoint of the original scene. To capture long-range object deformation, we introduce positional loss into the optimization process of 3D Gaussian Splatting and enable gradient propagation through reparameterization. To handle occluded 3D Gaussians when rendering from the specified viewpoint, we build an anchor-based structure and employ a coarse-to-fine optimization strategy capable of handling long-range deformation while maintaining structural stability. Furthermore, we design a novel masking strategy to adaptively identify non-rigid deformation regions for fine-scale modeling. Extensive experiments show the effectiveness of our method in handling geometric details, long-range, and non-rigid deformation, demonstrating superior editing flexibility and quality compared to previous approaches.

• The paper introduces a novel single-image-driven 3D scene editing method that maps 2D edits directly to 3D Gaussian representations.
• It employs positional derivatives and anchor-based ARAP regularization to capture long-range deformations while maintaining geometric consistency.
• A two-stage optimization coupled with adaptive rigidity masking enhances visual fidelity and ensures robust performance across diverse datasets.

3D Gaussian Editing with A Single Image

The paper "3D Gaussian Editing with A Single Image" by Guan Luo et al. provides a robust framework for intuitive and detailed 3D scene editing leveraging 3D Gaussian Splatting (3DGS). This research addresses key limitations in existing 3D content manipulation methods and introduces innovative strategies for scene alignment and deformation using single-image-driven inputs.

Core Contributions

1. Single-Image-Driven 3D Scene Editing

The authors present a novel method allowing users to edit 3D scenes directly via single 2D images. Compared to traditional approaches that rely on intricate and often imperfect 3D mesh reconstructions, this method simplifies the editing process by using 3DGS. The resulting approach exemplifies "what you see is what you get," where the edited 2D image directly guides the 3D scene's modifications.

2. Positional Derivatives and Long-Range Deformation

The paper highlights the insufficiency of conventional photometric losses in handling long-range deformations. To address this, the authors introduce positional loss into the optimization framework. This loss explicitly models long-range correspondence using optimal transport principles. The novel inclusion of positional derivatives enables the method to capture extensive object deformation, ensuring gradient propagation through reparameterization of 3D Gaussians.

3. Anchor-Based As-Rigid-As-Possible (ARAP) Regularization

To maintain geometric consistency, especially for occluded parts during scene editing, an anchor-based ARAP regularization method is proposed. This anchor-based structure leverages farthest point sampling (FPS) to derive sparse anchor points that effectively model the underlying 3D deformation fields. Consequently, this prevents undesired deformations and artifacts, ensuring robust convergence in fewer iterations.

4. Two-Stage Optimization

A coarse-to-fine optimization strategy enhances visual fidelity and structural stability. In the coarse stage, the method optimizes anchor points to capture overall deformation, while in the fine stage, it directly refines the 3D Gaussians' parameters. This staged approach mitigates boundary artifacts and improves texture detail accuracy.

5. Adaptive Rigidity Masking

Reflecting the non-uniform rigidity across different parts of real-world objects, the authors introduce an adaptive rigidity masking strategy. This mechanism employs learnable masks to identify and adaptively relax the regularization in regions undergoing non-rigid deformation, thus ensuring detailed and precise geometric modeling.

Evaluation and Results

Extensive experiments on multiple datasets, including NeRF Synthetic (NS), 3DBiCar, and real-world datasets like Mip-NeRF 360 and Tanks and Temples, validate the effectiveness of this approach. The method demonstrates superior performance in both alignment with edited images and consistency in novel view synthesis. Quantitative metrics (PSNR, SSIM, and LPIPS) highlight significant improvements over baseline methodologies, such as traditional 3DGS and DROT.

Implications and Future Directions

Practical Implications

The proposed method holds considerable promise for applications requiring efficient and precise 3D content manipulation, such as in film production, gaming, and augmented/virtual reality. By leveraging single-image inputs for 3D editing, this approach reduces the technological and labor-intensive barriers associated with traditional 3D content creation.

Theoretical Implications

The innovative use of positional derivatives within the 3DGS framework opens new research avenues in neural scene representations. The anchor-based regularization and adaptive masking strategies contribute significantly to the robustness and accuracy of 3D scene editing methodologies, setting a precedent for future studies in this domain.

Speculative Future Developments

Future research could explore the integration of semantic understanding into the editing process, thereby enabling more context-aware manipulation of 3D scenes. Additionally, enhancing the method's capability to handle dynamic scenes and improving texture editing resolution through disentangled representations could further broaden its applicability.

In summary, this paper makes substantial advancements in 3D scene editing with a single image by introducing a method that optimally aligns 3D Gaussian representations with user-specified 2D edits, while maintaining structural stability and fine-grained detail accuracy.