Make Your MoVe: Make Your 3D Contents by Adapting Multi-View Diffusion Models to External Editing

• URL: http://arxiv.org/abs/2508.07700v1
• Date: Mon, 11 Aug 2025 07:23:39 GMT
• Title: Make Your MoVe: Make Your 3D Contents by Adapting Multi-View Diffusion Models to External Editing
• Abstract summary: We propose a tuning-free, plug-and-play scheme that aligns edited assets with their original geometry in a single inference run.<n>Our method consistently improves both the multi-view consistency and mesh quality of edited 3D assets.
• Score: 26.563801961090544
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As 3D generation techniques continue to flourish, the demand for generating personalized content is rapidly rising. Users increasingly seek to apply various editing methods to polish generated 3D content, aiming to enhance its color, style, and lighting without compromising the underlying geometry. However, most existing editing tools focus on the 2D domain, and directly feeding their results into 3D generation methods (like multi-view diffusion models) will introduce information loss, degrading the quality of the final 3D assets. In this paper, we propose a tuning-free, plug-and-play scheme that aligns edited assets with their original geometry in a single inference run. Central to our approach is a geometry preservation module that guides the edited multi-view generation with original input normal latents. Besides, an injection switcher is proposed to deliberately control the supervision extent of the original normals, ensuring the alignment between the edited color and normal views. Extensive experiments show that our method consistently improves both the multi-view consistency and mesh quality of edited 3D assets, across multiple combinations of multi-view diffusion models and editing methods.

Related papers

• DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing [47.437239634403426]
  We present a novel personalization method for text-guided 3D editing that enables compositional, object-level control through natural language.<n>Our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation.
  arXiv  Detail & Related papers  (2026-05-16T13:21:22Z)
• Geometry-Guided Reinforcement Learning for Multi-view Consistent 3D Scene Editing [106.07976338405793]
  Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm.<n>We propose textbfRL3DEdit, a single-pass framework driven by reinforcement learning with novel rewards derived from the 3D foundation model, VGGT.<n>Experiments demonstrate that RL3DEdit achieves stable multi-view consistency and outperforms state-of-the-art methods in editing quality with high efficiency.
  arXiv  Detail & Related papers  (2026-03-03T16:31:10Z)
• Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow [29.8200628539749]
  We propose an effective and fully feedforward 3D editing framework based on the TRELLIS generative backbone.<n>Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features.
  arXiv  Detail & Related papers  (2026-02-25T02:15:14Z)
• Fast Multi-view Consistent 3D Editing with Video Priors [19.790628738739354]
  We propose generative Video Prior based 3D Editing (ViP3DE)<n>Our key insight is to condition the video generation model on a single edited view to generate other consistent edited views for 3D updating directly.<n>Our proposed ViP3DE can achieve high-quality 3D editing results even within a single forward pass, significantly outperforming existing methods in both editing quality and speed.
  arXiv  Detail & Related papers  (2025-11-28T13:31:10Z)
• Native 3D Editing with Full Attention [47.908091876301796]
  We propose a novel native 3D editing framework that directly manipulates 3D representations in a single, efficient feed-forward pass.<n>This dataset is meticulously curated to ensure that edited objects faithfully adhere to the instructional changes.<n>Our results demonstrate that token concatenation is more parameter-efficient and achieves superior performance.
  arXiv  Detail & Related papers  (2025-11-21T18:59:26Z)
• 3D-LATTE: Latent Space 3D Editing from Textual Instructions [64.77718887666312]
  We propose a training-free editing method that operates within the latent space of a native 3D diffusion model.<n>We guide the edit synthesis by blending 3D attention maps from the generation with the source object.
  arXiv  Detail & Related papers  (2025-08-29T22:51:59Z)
• MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors [19.950368071777092]
  Existing 3D drag-based editing methods fall short in handling significant topology changes or generating new textures across diverse object categories. We introduce MVDrag3D, a novel framework for more flexible and creative drag-based 3D editing. We show that MVDrag3D provides a precise, generative, and flexible solution for 3D drag-based editing.
  arXiv  Detail & Related papers  (2024-10-21T17:59:53Z)
• Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning [52.81032340916171]
  Coin3D allows users to control the 3D generation using a coarse geometry proxy assembled from basic shapes. Our method achieves superior controllability and flexibility in the 3D assets generation task.
  arXiv  Detail & Related papers  (2024-05-13T17:56:13Z)
• View-Consistent 3D Editing with Gaussian Splatting [50.6460814430094]
  View-consistent Editing (VcEdit) is a novel framework that seamlessly incorporates 3DGS into image editing processes.<n>By incorporating consistency modules into an iterative pattern, VcEdit proficiently resolves the issue of multi-view inconsistency.
  arXiv  Detail & Related papers  (2024-03-18T15:22:09Z)
• Plasticine3D: 3D Non-Rigid Editing with Text Guidance by Multi-View Embedding Optimization [21.8454418337306]
  We propose Plasticine3D, a novel text-guided controlled 3D editing pipeline that can perform 3D non-rigid editing. Our work divides the editing process into a geometry editing stage and a texture editing stage to achieve separate control of structure and appearance. For the purpose of fine-grained control, we propose Embedding-Fusion (EF) to blend the original characteristics with the editing objectives in the embedding space.
  arXiv  Detail & Related papers  (2023-12-15T09:01:54Z)
• Efficient-NeRF2NeRF: Streamlining Text-Driven 3D Editing with Multiview Correspondence-Enhanced Diffusion Models [83.97844535389073]
  A major obstacle hindering the widespread adoption of 3D content editing is its time-intensive processing. We propose that by incorporating correspondence regularization into diffusion models, the process of 3D editing can be significantly accelerated. In most scenarios, our proposed technique brings a 10$times$ speed-up compared to the baseline method and completes the editing of a 3D scene in 2 minutes with comparable quality.
  arXiv  Detail & Related papers  (2023-12-13T23:27:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.