mlsanityData to 14 Oct 2026

Velocity-Space 3D Asset Editing

arXiv 2605.07385 ↗First posted 8 May 2026cs.GR cs.CVcited by 2

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Abstract (arXiv)

Editing a 3D asset locally, modifying a target region while preserving the rest, is a fundamental requirement of native 3D editing. Existing methods enforce locality through mechanisms external to the generator, such as manual 3D masks, post-hoc voxel merging, or 2D multi-view lifting. None of them intervene where the corruption actually originates: inside the ODE sampler. For a rectified-flow generator to achieve faithful local editing, its velocity field should be strong over the target editing region while vanishing on preserved content. Yet a single velocity field can hardly satisfy both requirements simultaneously, leading to three problems: (i) identity leakage that keeps the edit signal non-zero on preserved regions; (ii) no dedicated edit-amplification channel, so strengthening the edit inevitably perturbs identity; and (iii) an identity drag at the geometry and material stages, where a global condition pulls every token toward the target. We propose VS3D (Velocity-Space 3D Asset editing}), an inversion-free, training-free, and mask-free framework that addresses each problem with a targeted intervention inside the sampler. VS3D integrates three complementary modules, each corresponding to a specific stage of the editing pipeline. Reconstruction-Anchored Source Injection (RASI) absorbs identity leakage by turning the unconditional embedding into a per-step, asset-specific anchor calibrated through source reconstruction. Partial-Mean Guidance (PMG) amplifies the edit signal by contrasting high- and low-quality subsample estimates of the velocity difference, active only where a consistent edit exists. Twin-Agreement Residual injection (TAR) lets the sampler decide token by token what to preserve at the geometry and material stages.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 21 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

21 of 21 references.

References · 21

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2022Flow Matching for Generative Modelingcited by 5,702
  4. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  5. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
  6. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,179
  7. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
  8. 2024FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Modelscited by 217
  9. 2025Native and Compact Structured Latents for 3D Generationcited by 175
  10. 2025Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Detailscited by 125
  11. 2025Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attentioncited by 110
  12. 2025Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modelingcited by 89
9 more
  1. 2025VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Spacecited by 45
  2. 2024Instant3dit: Multiview Inpainting for Fast Editing of 3D Objectscited by 44
  3. 2025LATTICE: Democratize High-Fidelity 3D Generation at Scalecited by 44
  4. 2025NANO3D: A Training-Free Approach for Efficient 3D Editing Without Maskscited by 31
  5. 2025Edit360: 2D Image Edits to 3D Assets from Any Anglecited by 13
  6. 2026Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flowcited by 9
  7. 2026VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Imagecited by 4
  8. 2026ShapeUP: Scalable Image-Conditioned 3D Editingcited by 4
  9. 2026Vinedresser3D: Agentic Text-guided 3D Editingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.