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3D Gaussian Editing with A Single Image

Guan Luo, Tian-Xing Xu, Ying-Tian Liu, Xiao-Xiong Fan, Fang-Lue Zhang, Song-Hai Zhang

arXiv:2408.07540v1cs.CVcs.MM

TL;DR

Mesh-based editing often depends on accurately reconstructed geometry, while other scene representations can limit explicit manipulation or detailed user control. This paper edits 3D Gaussian scenes against a user-edited image, combining positional loss, anchor-based regularization, coarse-to-fine optimization, and adaptive masking. Experiments report greater editing flexibility and quality than previous approaches, with limitations in weak-texture matching and texture-editing resolution.

  • Problem

    Mesh-based editing often requires accurately reconstructed meshes, while implicit representations can impede explicit manipulation and some editing approaches limit detailed user control.

    Source: §1 · p. 2

  • Method

    The method optimizes 3D Gaussians to align with a user-edited image, using positional loss, anchor-based ARAP regularization, coarse-to-fine optimization, and adaptive rigidity masking.

    Source: §1 · p. 2

  • Results

    Experiments demonstrate superior editing flexibility and quality compared with previous approaches.

    Source: §6 · p. 8

  • Takeaways & Limitations

    The approach addresses geometric details, long-range deformation, and non-rigid editing in single-image-driven 3D scene manipulation.

    Source: §1 · p. 2, §6 · p. 8

    Weak texture can cause Sinkhorn divergence to produce incorrect pixel matches, and driving Gaussians rather than growing and pruning limits texture-editing resolution.

    Source: §6 · p. 8

Abstract

from arXiv · show

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.

1 INTRODUCTION

The introduction motivates explicit 3D Gaussian editing as a response to limitations in mesh-based and implicit-representation editing. It presents a single-image-driven method using positional loss, anchor-based regularization, coarse-to-fine optimization, and adaptive rigidity masking.

1 INTRODUCTION

  • The approach edits 3D Gaussian scenes from a user-provided edited image rendered from a specified viewpoint, offering direct image-driven control.The paper frames this as “what you see is what you get.”Source: §1 · p. 2
  • Positional loss and reparameterization introduce long-range motion guidance and enable gradient propagation through 3D Gaussian optimization.The method draws inspiration from optimal transport to model long-range correspondence explicitly.Source: §1 · p. 2
  • Anchor-based ARAP regularization and coarse-to-fine optimization aim to maintain object-level geometric consistency during editing.The anchors capture the deformation field, while the optimization strategy enhances edited-result fidelity.Source: §1 · p. 2
  • Adaptive masking identifies non-rigid deformation regions and releases ARAP regularization there for more precise geometric-detail modeling.The strategy responds to objects having different levels of rigidity.Source: §1 · p. 2

2 RELATED WORK

Prior differentiable-rendering work uses positional correspondence to model motion, while NeRF and 3D Gaussian editing methods use mesh, semantic, or appearance-based approaches with limitations in deformation and fine-grained control.

2.1 Differentiable Rendering

  • DROT adds positional loss to RGB losses using pixel-level screen-space correspondence to model global and long-range object motion.It integrates optimal transport into differentiable rendering.Source: §2.1 · p. 2

2.2 NeRF and 3D Gaussian Editing

  • Mesh-extraction and cage-based editing methods [28] [53] [55] [26] [51] are limited by reconstructed geometry and struggle with non-rigid deformation.Text-guided editing approaches [2] [9] [12] [17] [18] [19] [33] [42] [43] [46] [10] [13] [36] [49] cannot provide detailed geometry and texture editing.Source: §2.2 · pp. 2–3
  • Other approaches use feature points [8], modify color bases [16] [24], or bind Gaussians to meshes [30] [54] [57], with stated trade-offs in labor, fine-grained guidance, or mesh dependence.Knowledge distillation [48] supports multiview appearance consistency but only rigid transformations; geometry-texture disentanglement [52] targets efficient texture editing.Source: §2.2 · pp. 2–3

3 PRELIMINARIES

The preliminaries describe 3D Gaussian Splatting as a scene representation rendered by projecting and blending Gaussian contributions in image space.

3 PRELIMINARIES

  • Each 3D anisotropic Gaussian is represented by a center, covariance matrix, opacity, and RGB color.The covariance is defined in world space, and color is represented using spherical harmonics.Source: §3 · p. 3
  • The covariance is parameterized using rotation and scaling so it remains positive semi-definite during optimization.Rotation is represented by a quaternion and scaling by a 3D vector.Source: §3 · p. 3
  • EWA splatting projects each Gaussian onto the image plane, producing a 2D covariance from its 3D covariance and the viewing transformation.The projection uses the perspective transformation's affine approximation.Source: §3 · p. 3
  • The renderer aggregates colors from overlapping Gaussians through alpha blending.The alpha value depends on Gaussian opacity and its projected filter.Source: §3 · p. 3

4 METHOD

The method optimizes 3D Gaussians to match an edited reference image, adding positional guidance for long-range motion and regularization strategies for occluded and non-rigid regions. Its optimization combines anchor-based coarse deformation with fine Gaussian-level refinement.

4 METHOD

  • The method optimizes 3D Gaussian parameters to align a rendered scene with an edited reference image, addressing long-range motion and occluded geometry.Pixel-wise L1 and SSIM losses alone provide local derivatives and are less effective for long-range translation and deformation.Source: §4 · p. 3

4.1 Positional Derivative

  • Optimal-transport correspondences define a positional loss whose reparameterized gradients guide Gaussian updates; these gradients point the bulldozer blade downward more accurately than photometric-loss gradients.Pixel centers are matched between rendered and target images using color and positional distance; reparameterization allows gradients to pass through Gaussian sampling.Source: §4.1 · pp. 3–4, Figure 3 · p. 4

4.2 Anchor-Based Deformation

  • Sparse anchors model the deformation field and reduce iterations for occluded regions; coarse anchor updates capture long-range changes, followed by fine Gaussian updates to refine geometry and color.The fine stage also applies ARAP regularization to Gaussians, while the coarse stage applies it to anchors.Source: §4.2 · pp. 4–5

4.3 Adaptive Rigidity Masking

  • Learnable rotation and distance masks adaptively relax ARAP regularization in non-rigid regions, where different parts may require different rigidity levels.In examples, distance masks for stretched material balls and ARAP masks for a microphone joint approach zero after optimization.Source: §4.3 · p. 5

4.4 Loss Function

  • The matching loss combines positional, L1, and SSIM terms; mask L1 regularization discourages collapse, while fine-stage geometric and texture edits receive additional scale and color regularization.The fine stage regularizes Gaussian scales for geometric editing and colors for texture editing.Source: §4.4 · pp. 5–6

5 EXPERIMENT

The experiments evaluate reference-view alignment, novel-view synthesis, geometric and hybrid editing, and single-view video tracking across synthetic and real-world scenes. Results report stronger benchmark performance, long-range deformation handling, and structural consistency across editing settings.

5.1 Long-range Deformation

  • With positional loss, objects reach target positions despite no initial-target overlap, and the method captures large deformations that 3DGS misses.For short-range deformation, both 3DGS and the proposed method recover the deformation correctly.Source: §5.1 · p. 6

5.2 Geometry Editing

  • Scene-level editing applies 2D edits to a rendered region of interest and back-propagates them to the 3D scene.Against DROT and Deforming-NeRF, the method aligns precisely while maintaining 3D consistency; DROT can destabilize occluded geometry, while cage resolution limits Deforming-NeRF on stretching.Source: §5.2 · p. 6
  • Our method outperforms vanilla 3DGS and DROT in reference-view alignment and novel-view synthesis, with consistent and significant metric improvements.Table 1 reports average PSNR/SSIM/LPIPS for reference-view alignment on NS and novel-view synthesis on 3DBiCar.Source: §5.2 · p. 6, Table 1 · p. 6

5.3 Hybrid Editing

  • Hybrid editing jointly changes geometry and texture in one optimization, with anchor updates modeling long-range deformation before Gaussian-level refinement.Examples include moving a LEGO pillar, elongating a cockpit, and stretching a chair; the reported results remain promising in complex cases.Source: §5.3 · p. 7, Figure 9 · p. 7

5.4 Single-View Video Tracking

  • Using only the coarse stage, single-view video tracking captures long-range motion while maintaining spatial and temporal consistency and producing novel-view results.The setup starts from a 3D Gaussian scene reconstructed from multi-view images and optimizes anchor positions and rotations for fast convergence.Source: §5.4 · p. 7, Figure 10 · p. 8

5.5 Ablation Study

  • Ablations show that positional loss, two-stage optimization, and adaptive rigidity masking improve target-view alignment and novel-view synthesis; explicit relative rotation and distance regularization supports structural stability.The coarse stage captures long-range deformation and rough alignment; the fine stage reduces boundary artifacts and models texture detail. Relative rotation and position regularization also addresses needlelike problems and structural errors.Source: §5.5 · p. 8

6 CONCLUSION AND LIMITATION

The method edits 3D scenes from a single image through iterative 3D Gaussian Splatting optimization, with mechanisms for long-range deformation, occluded Gaussians, and fine-grained detail. Its limitations include unreliable pixel matching in weak-texture areas and constrained texture-editing resolution.

6 CONCLUSION AND LIMITATION

  • The approach combines positional loss, reparameterized differentiation, anchor-based As-Rigid-As-Possible regularization, coarse-to-fine optimization, and rigidity masking for detailed single-image 3D scene editing.Experiments show superior editing flexibility and quality compared to previous approaches.Source: §6 · p. 8
  • Weak-texture areas can cause Sinkhorn divergence to provide incorrect pixel matches, affecting optimization of the underlying 3D scene.The positional loss uses optimal transport and is limited by pixel-matching accuracy.Source: §6 · p. 8
  • Texture-editing resolution is limited because the method drives existing 3D Gaussians rather than growing and pruning them.The authors suggest disentangling geometry and texture may improve texture-editing quality.Source: §6 · p. 8

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