Multi-View Image Editing

• Multi-view image editing is a technique that produces 3D-consistent outputs by coordinating modifications across multiple viewpoints.
• It leverages advanced methods, such as 3D representation learning, diffusion models, and attention-based fusion, to enforce geometric and semantic consistency.
• Emerging approaches focus on efficiency, training-free pipelines, and enhanced user controllability for applications in XR, robotics, and digital content creation.

Multi-view image editing refers to the set of methods and systems enabling consistent and controlled modification of objects or scenes across multiple images taken from different viewpoints, with the goal of producing 3D-consistent visual outputs. This challenge arises in graphics, vision, XR, robotics, and content generation, where modifications (such as object insertion, stylization, deformation, or relighting) are required to remain geometrically and semantically consistent across a collection of views or an entire 3D asset. The field leverages advances in 3D representation learning, 2D and 3D diffusion models, explicit and implicit scene representations, attention-based fusion, and optimization-based consistency enforcement.

1. Foundational Motivation and Challenges

Multi-view image editing seeks to overcome fundamental limitations of per-image editing, which often yields view-dependent inconsistencies, ghosting, or geometric misalignments. This is especially severe when 2D edits are made independently across a set of views, resulting in artifacts when assembling images into 3D reconstructions, rendering dynamic scenes, or training downstream perception systems. The core challenges include:

• Enforcing geometric and appearance consistency across widely varying viewpoints, especially when edits affect geometry, occlusions, or appearance.
• Efficiently propagating edits from a reference (e.g., a single view or prompt) to the remaining novel views and handling ambiguous or occluded regions.
• Avoiding slow, unstable optimization (as in iterative per-view methods or unreliable pixel-inpainting pipelines).
• Adapting to sparse or dense multi-view configurations, working under limited supervision, and achieving generalizability across diverse content.

Traditional methods based on 3D representations (e.g., NeRFs, meshes, point clouds) require lengthy optimization and often exhibit slow convergence, while 2D-based approaches lack inherent cross-view awareness—necessitating explicit mechanisms for consistency.

2. Multi-View Fusion Networks and Latent Alignment

Early solutions for multi-view editing, such as the multi-view fusion network in human reposing and compositing (Jain et al., 2022), aggregate pose and texture information from multiple source images to produce spatially coherent edits. These networks operate by:

• Extracting pose keypoints for each source view, denoted by $P_i = \{(x^j_i, y^j_i)\}_{j=1}^K$;
• Encoding textures (e.g., using UNet-based encoders) into deep feature maps $F_i = E(I_i, P_i)$;
• Computing an explainable per-pixel appearance retrieval map $R(x,y)$ via attention or similarity between the target feature and each source:

$$s_i(x, y) = \frac{\langle f_{\text{target}}(x, y), F_i(x, y) \rangle}{\|f_{\text{target}}(x, y)\| \|F_i(x, y)\|}$$

$$R_i(x, y) = \frac{\exp(s_i(x, y))}{\sum_{j=1}^N \exp(s_j(x, y))}$$

• Fusing latent features as

$$F_{\text{fused}}(x, y) = \sum_{i=1}^N R_i(x, y) \cdot F_i(x, y)$$

• Decoding $F_{\text{fused}}$ to generate the final edit.

This explicit pixel-to-view attribution mitigates issues of occlusion and enables tasks such as pose reposing or composite "Mix&Match" generation, providing mechanisms for spatial explainability often missing in single-view approaches.

3. Explicit and Implicit 3D Consistency Models

State-of-the-art approaches use explicit 3D representations (e.g., 3D Gaussian Splatting (Wu et al., 13 Mar 2024, Wang et al., 18 Mar 2024, Lee et al., 16 Dec 2024); Neural Radiance Fields (Patashnik et al., 22 Feb 2024)) coupled with diffusion editing models to address spatial and semantic consistency:

• 3D Gaussian Splatting (3DGS): Scenes are modeled as mixtures of anisotropic Gaussians, each parameterized by position, covariance, color, and opacity. Rendering integrates the contributions along a ray:

$$I(x) = \sum_{i=1}^N w_i(x) \cdot c_i, \quad w_i(x) = \exp(-(x-\mu_i)^T \Sigma_i^{-1} (x-\mu_i))$$

Edits are performed either by optimizing Gaussians to match target views (Lee et al., 16 Dec 2024) or by pruning/updating based on attention maps projected onto the 3D structure.

• Latent Consistency: Methods like GaussCtrl (Wu et al., 13 Mar 2024) use depth-conditioned editing and cross-view attention, propagating reference-guided, semantically meaningful edits through both self- and cross-attention across latent codes. TrAME (Luo et al., 2 Jul 2024) achieves iterative improvement by tightly coupling 2D view edits with 3D updates using a trajectory-anchored scheme, preventing error accumulation with progressive feedback cycles.
• Attention-based cross-view regularization: Techniques such as QNeRF (Patashnik et al., 22 Feb 2024) consolidate diffusion attention queries via a volumetric field trained on query vectors, then reinject these consistent queries during generation; VcEdit (Wang et al., 18 Mar 2024) reconstructs 3D attention distributions and enforces view-space consistency via inverse and forward rendering of cross-attention maps.

4. From 2D Lifted Editing to Training-Free Inference-Time Methods

The 2D-lifting paradigm, as in C³Editor (Tao et al., 6 Oct 2025), employs 2D diffusion models to edit arbitrary viewpoints of a rendered 3D model, but explicit mechanisms are necessary to ensure that edits are consistent across the (potentially large) set of views:

• C³Editor introduces user-controlled GT (ground truth) view selection and dedicated Low-Rank Adaptation modules (LoRA) to fine-tune for both intra-view accuracy and inter-view coherence. The process involves propagation of the GT edit to all viewpoints (ordered by geometric adjacency), minimizing joint losses that balance quality and uniformity.

Recent advances, such as Coupled Diffusion Sampling (Alzayer et al., 16 Oct 2025), avoid any explicit 3D optimization or model retraining. By concurrently sampling with a pre-trained 2D editing model and a multi-view diffusion prior, and introducing a coupling energy

$U(x, x') = -\frac{\lambda}{2} \|x - x'\|_2^2$

the edit trajectory is dynamically pulled towards the space of consistent multi-view images, i.e.,

$$x_{t-1}^A = \text{DDPM\_update}(x_t^A, \dots) - \sqrt{1-\alpha_{t-1}} \lambda (\hat{x}_0^A - \hat{x}_0^B)$$

This implicit regularization produces spatially and semantically consistent results without the need for 3D reconstruction, and can be generalized across various backbone architectures.

5. Future Directions and Applications

Research in multi-view image editing extends into domains such as robotics (ERMV (Nie et al., 23 Jul 2025)), where 4D spatio-temporal consistency must be maintained in long sequential data, and driving simulation (SceneCrafter (Zhu et al., 24 Jun 2025)), where global and local conditions (e.g., weather, agent boxes) are incorporated as conditioning signals in multi-view diffusion models.

Key open challenges and research directions include:

• Richer propagation mechanisms: From anchor-view fusion (Edit360 (Huang et al., 12 Jun 2025)) to reference-guided multi-view inpainting (MVInpainter (Cao et al., 15 Aug 2024)), methods increasingly leverage spatial-temporal priors, flow grouping, and cross-view attention for coherent propagation, often with pose-free architectures for greater generality.
• Explicit versus implicit regularization: A spectrum exists between direct 3D representation optimization and strictly 2D regularization, with hybrid approaches exploiting both (DisCo3D (Chi et al., 3 Aug 2025), EditSplat (Lee et al., 16 Dec 2024)).
• Efficiency and scalability: Recent methods prioritize training-free, plug-and-play pipelines, avoiding per-scene optimization and allowing rapid or even real-time editing (Tinker (Zhao et al., 20 Aug 2025), PosBridge (Xiong et al., 24 Aug 2025)).
• User controllability: Incorporation of user-selected anchor or GT views, explicit masks, and image or text prompts allows for more precise and fine-grained control over the editing process.
• Applicability: The ability to achieve multi-view consistent editing is now critical for AR/VR scene authoring, digital content pipelines, vision-LLM data augmentation, customized 3D asset creation, and simulation scenarios requiring environment controllability.

6. Comparative Summary of Approaches

Approach	Main Consistency Mechanism	Editing Modality
Multi-view fusion networks (Jain et al., 2022)	Per-pixel appearance attention, fusion	Pose+texture, keypoints
3DGS-based methods (Wu et al., 13 Mar 2024Lee et al., 16 Dec 2024)	Explicit 3D (Gaussian) consistency, attention-based pruning	Text-driven
Latent coupling (Alzayer et al., 16 Oct 2025)	Coupled diffusion sampling (implicit), multi-view prior	Training-free, 2D models
Key/anchor-view propagation (Huang et al., 12 Jun 2025Zheng et al., 31 May 2025)	Progressive or anchor-view fusion, mixture-of-experts	Guided per-view
Robotics/editing (Nie et al., 23 Jul 2025)	Epipolar motion-aware attention, feedback	4D sequence, state+mask
EditP23 (Bar-On et al., 25 Jun 2025)	Edit-aware latent flow, mask-free, prompt-based	User-edited image pair

The above table highlights selected methods (not exhaustive) and their primary technical contribution.

7. Outlook and Implications

Multi-view image editing has advanced from early fusion networks with pose and texture aggregation to sophisticated pipelines leveraging explicit 3D structures, multi-view attention, latent consistency, and training-free sampling. The field is defined by its pursuit of reliability (consistency across generated and rendered views), efficiency (real-time, scalable inference), and controllability (guided or user-driven modification). Current methods yield robust performance in high-fidelity, geometry-aware tasks spanning content creation, simulation, and vision. Future work is likely to involve tighter cross-modal integration, improved efficiency, richer user interaction, and adaptation to higher-dimensional (temporal, physically-based) scene editing problems.