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GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing

Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, Victor Adrian Prisacariu

arXiv:2403.08733v4cs.CV

TL;DR

Text-driven editing of reconstructed 3D scenes remains challenging because prior methods do not ensure consistency across views. GaussCtrl edits views together using depth-conditioned editing and attention-based latent-code alignment. The paper reports improved visual quality and reduced processing time compared with prior work.

  • Problem

    Prior text-driven 3D editing methods do not encourage consistency across views, which is important for avoiding visual artifacts such as blur and inconsistent appearance.

    Source: §1 · p. 2

  • Method

    GaussCtrl edits rendered views together, using depth-conditioned editing and self- and cross-view attention to align latent codes with selected reference views.

    Source: §1 · pp. 2–3

  • Results

    GaussCtrl reports outperforming other state-of-the-art methods across diverse scenes, text prompts, and objects, with improved visual quality and reduced processing time.

    Source: §1 · p. 3, §5 · p. 14

  • Takeaways & Limitations

    The method controls consistency through depth-conditioned image editing and attention-based latent-code alignment across editing stages.

    Source: §5 · p. 14

    The final result is not always faithful to the user’s intention, and the method fails on some requested geometry or character transformations.

    Source: §4.5 · p. 14

Abstract

from arXiv · show

We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed by the 3D Gaussian Splatting (3DGS). Our method first renders a collection of images by using the 3DGS and edits them by using a pre-trained 2D diffusion model (ControlNet) based on the input prompt, which is then used to optimise the 3D model. Our key contribution is multi-view consistent editing, which enables editing all images together instead of iteratively editing one image while updating the 3D model as in previous works. It leads to faster editing as well as higher visual quality. This is achieved by the two terms: (a) depth-conditioned editing that enforces geometric consistency across multi-view images by leveraging naturally consistent depth maps. (b) attention-based latent code alignment that unifies the appearance of edited images by conditioning their editing to several reference views through self and cross-view attention between images' latent representations. Experiments demonstrate that our method achieves faster editing and better visual results than previous state-of-the-art methods.

1 Introduction

Text-driven editing of reconstructed 3D scenes is emerging, but prior methods do not adequately ensure consistency across views. GaussCtrl edits views together using depth guidance and latent-code alignment, with reported gains in visual quality and processing time.

1 Introduction

  • Prior methods edit rendered images iteratively while updating the 3D scene, but do not encourage multi-view consistency, which can leave artifacts and slow convergence.IN2N requires many full NeRF optimisations; inconsistencies remain in follow-up methods.Source: §1 · p. 2
  • GaussCtrl edits all images together and updates the 3D model only once, using depth-controlled editing and latent-code alignment to encourage multi-view consistency.Editing is conditioned on naturally geometry-consistent depth maps.Source: §1 · p. 2
  • During denoising, self- and cross-view attention align other views’ latent codes to selected reference views to encourage appearance consistency.The paper says this improves high-level semantic consistency across the dataset.Source: §1 · p. 3
  • GaussCtrl reports more realistic, higher-quality editing than previous work across a variety of 3D editing scenes.The paper also reports significantly improved visual quality and reduced processing time.Source: §1 · p. 3

2 Related Works

Prior 2D diffusion editing methods do not address multi-view consistency, while 3D editing approaches include iterative image editing and mesh optimisation. GaussCtrl targets consistency and visual quality in text-driven 3D editing.

2.1 2D Editing with Diffusion Models

  • Prior 2D diffusion editing methods do not consider multi-view consistency; GaussCtrl addresses this with latent-code control and attention-based alignment.The paper identifies inconsistent editing as a potential source of artifacts.Source: §2.1 · p. 4

2.2 3D Editing in NeRF and Gaussian Splatting

  • IN2N iteratively edits rendered images and updates a NeRF, while ViCA-NeRF blends projected reference edits and DreamEditor optimises a converted mesh.The paper says ViCA-NeRF’s blending does not fully resolve consistency and produces blurry editing.Source: §2.2 · p. 4

3 Method

GaussCtrl edits rendered views of a reconstructed 3DGS scene with depth-conditioned ControlNet and reference-view attention, then uses the edited images to optimise the original model. Depth maps target geometric consistency, while latent attention aligns appearance across views.

3 Method

  • GaussCtrl re-renders dataset images and depth maps, edits all images with ControlNet and latent-code alignment, then optimises the original 3D model.A mask from Language-based Segment Anything may optionally filter the background for local-object editing.Source: §3 · pp. 4–5

3.1 Background

  • 3D Gaussian Splatting represents scenes as point-cloud-based sets of 3D Gaussian ellipses, each with a colour and opacity; ControlNet adds spatial conditioning to Stable Diffusion, including depth.Source: §3.1 · p. 5

3.2 Depth-conditioned Image Editing

  • ControlNet conditions editing on naturally cross-view-consistent scene depths; DDIM inversion supplies image-derived noise before edited-prompt denoising to promote geometric consistency.Source: §3.2 · p. 6

3.3 Attention-based Latent Code Alignment

  • Reference-view cross-attention aligns edited latent appearances, while self-attention preserves each image’s distinctiveness, reducing appearance discrepancies and anomalies at challenging viewpoints.Source: §3.3 · p. 7

4 Experiments

Experiments compare GaussCtrl across 360-degree and forward-facing scenes using qualitative results, CLIPdir and editing-time evaluation, component ablations, and limitation cases.

4.2 Qualitative Evaluation

  • Examples show realistic, consistent edits spanning global scene changes, local object edits, and changes to specific colors or textures.Examples include a dinosaur in snow, a polar bear in a forest, red hair, and Chinese opera face paint.Source: Figure 3 · p. 8, §4.2 · p. 10

4.1 Experiment Setup

  • GaussCtrl is evaluated on four 360-degree and two forward-facing scenes, compared with IN2N(GS) and ViCA-NeRF, with CLIPdir used to assess the alignment of 3D edits.All methods are evaluated on the same data after images are preprocessed to 512×512; GaussCtrl takes around 9 minutes to edit one scene on an NVIDIA RTX A5000 with 24GB GPU memory.Source: §4.1 · pp. 8–9

4.2 Qualitative Evaluation

  • In 360-degree comparisons, GaussCtrl produces sharper, more consistent object and environment edits than baselines that show incomplete or blurry results.IN2N(GS) has incomplete local edits, while ViCA-NeRF shows blur; the paper attributes these artifacts to blending inconsistently edited reference images.Source: Figure 4 · p. 9, §4.2 · p. 10
  • In forward-facing scenes, baselines are more consistent than in 360-degree scenes, but still show blurry boundaries while GaussCtrl produces more realistic results.The paper attributes the baseline consistency difference to less extreme viewpoint variation.Source: Figure 5 · p. 10, §4.2 · p. 11

4.3 Quantitative Evaluation

  • GaussCtrl outperforms other approaches in four of six scenes on average CLIPdir and is the fastest method.The paper cautions that CLIPdir can miss local editing quality.Source: Table 1 · p. 11, §4.3 · p. 12

4.2 Qualitative Evaluation

  • Across 10 bear-scene views, latent-code alignment mitigates inconsistent details and the face-on-the-back problem seen in both baselines.The paper links the face-on-the-back issue to Instruct Pix2Pix forcing a polar bear into the image layout.Source: §4.2 · p. 11, Figure 7 · p. 12

4.4 Ablation Study

  • Ablations show that depth guidance preserves geometric consistency, inversion improves style alignment, and attention-based alignment mitigates artifacts and unifies appearance.With random noise, ControlNet diverges from the original style; cross-view attention conditions editing on reference views.Source: §4.4 · p. 13, Figure 8 · p. 13

4.5 Limitations

  • Depth guidance limits large geometry changes, and the method can also fail when ControlNet does not produce the intended semantic edit.The bear-to-giraffe geometry edit fails for GaussCtrl and the baselines; the Hulk edit failure is attributed to ControlNet not recognizing the word.Source: §4.5 · p. 14, Figure 9 · p. 14

5 Conclusion

GaussCtrl is an efficient 3D-aware consistency-control editing method that mitigates artifacts and blurry results from inconsistent 2D editing, especially in 360-degree scenes. The paper reports outperforming other state-of-the-art methods and notes that 3D editing could be misused to create deceptive or harmful content.

5 Conclusion

  • GaussCtrl outperforms other state-of-the-art methods across the evaluated diverse scenes, prompts, and objects.The method especially mitigates artifacts and blurry results from inconsistent 2D editing in 360-degree scenes.Source: §5 · p. 14
  • GaussCtrl controls consistency during depth-conditioned image editing and attention-based latent code alignment, using a pre-captured Gaussian model.These stages are the paper's stated means of controlling multi-view consistency.Source: §5 · p. 14
  • 3D editing methods such as GaussCtrl could be misused to create deceptive or harmful content, including hyper-realistic alterations and deepfakes.The paper connects this risk to misinformation, impersonation, harassment, and defamation.Source: §5 · p. 14

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