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Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion

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arxiv 2303.15780 v1 pith:HEHEGDAX submitted 2023-03-28 cs.CV

Hiromichi Kamata , Yuiko Sakuma , Akio Hayakawa , Masato Ishii , Takuya Narihira This is my paper · ORCID
classification cs.CV
keywords d-to-3dmethodsceneconversioninstructhigh-qualityproposeproposed
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D. Our method is designed for a novel task, which is to convert a given 3D scene to another scene according to text instructions. Instruct 3D-to-3D applies pretrained Image-to-Image diffusion models for 3D-to-3D conversion. This enables the likelihood maximization of each viewpoint image and high-quality 3D generation. In addition, our proposed method explicitly inputs the source 3D scene as a condition, which enhances 3D consistency and controllability of how much of the source 3D scene structure is reflected. We also propose dynamic scaling, which allows the intensity of the geometry transformation to be adjusted. We performed quantitative and qualitative evaluations and showed that our proposed method achieves higher quality 3D-to-3D conversions than baseline methods.

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Cited by 6 Pith papers

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

  1. CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Control

    cs.GR 2026-05 unverdicted novelty 7.0 of 10

    CompoSE synthesizes part-separated 3D objects from coarse geometric primitives via a part-aware diffusion transformer, enabling compositional editing operations like substitution and resizing without part-level text prompts.

  2. RelaxFlow: Text-Driven Amodal 3D Generation

    cs.CV 2026-03 conditional novelty 6.5 of 10

    A training-free dual-branch flow method uses multi-prior consensus and attention-logit low-pass relaxation to text-steer occluded 3D geometry while preserving the observed image.

  3. EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Mask-guided differential flow with a soft preservation loss enables training-free local 3D editing that keeps unedited regions close to the source asset.

  4. 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.

  5. SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training

    cs.CV 2025-12 conditional novelty 5.0 of 10

    A test-time-trained feedforward model that propagates 2D edits onto 3D Gaussian attributes at interactive speeds.

  6. InterGSEdit: Interactive 3D Gaussian Splatting Editing with 3D Geometry-Consistent Attention Prior

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A user-guided 3D scene editing method that builds a 3D attention prior from a selected key view and adaptively fuses it with 2D attention to improve multi-view consistency.

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