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3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting

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arxiv 2405.18424 v1 pith:STBUC3XV submitted 2024-05-28 cs.CV

classification cs.CV
keywords editingsceneditscenecontroldisentangledindividualgaussiangaussians
verification ladder T0 review T1 audit T2 compute T3 formal
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Scene image editing is crucial for entertainment, photography, and advertising design. Existing methods solely focus on either 2D individual object or 3D global scene editing. This results in a lack of a unified approach to effectively control and manipulate scenes at the 3D level with different levels of granularity. In this work, we propose 3DitScene, a novel and unified scene editing framework leveraging language-guided disentangled Gaussian Splatting that enables seamless editing from 2D to 3D, allowing precise control over scene composition and individual objects. We first incorporate 3D Gaussians that are refined through generative priors and optimization techniques. Language features from CLIP then introduce semantics into 3D geometry for object disentanglement. With the disentangled Gaussians, 3DitScene allows for manipulation at both the global and individual levels, revolutionizing creative expression and empowering control over scenes and objects. Experimental results demonstrate the effectiveness and versatility of 3DitScene in scene image editing. Code and online demo can be found at our project homepage: https://zqh0253.github.io/3DitScene/.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TRACE: High-Fidelity 3D Scene Editing via Tangible Reconstruction and Geometry-Aligned Contextual Video Masking

    cs.CV 2026-04 conditional novelty 6.0 of 10

    TRACE anchors a video-diffusion editor to 3D meshes to perform consistent part-level edits on 3D Gaussian scenes in about 10 minutes per edit.

  2. Edit in 2D, Verify in 3D: Reinforcement Learning for Multi-view Consistent Scene Editing

    cs.CV 2026-03 conditional novelty 6.0 of 10

    RL3DEdit fine-tunes FLUX-Kontext with GRPO using VGGT confidence and pose rewards to produce multi-view consistent 3D scene edits in a single pass.

  3. High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A 3D Gaussian inpainting framework with automatic mask refinement and depth-initialized uncertainty weighting balances multi-view consistency and visual detail, reporting the best LPIPS on the SPIn-NeRF dataset.

  4. Impact-driven Context Filtering For Cross-file Code Completion

    cs.SE 2025-08 unverdicted novelty 5.0 of 10

    The manuscript's abstract claims a new code-completion filtering method, yet the body contains an unrelated 3D animation paper, leaving the claimed work unverifiable.

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