Title: GeoComplete: Geometry-Aware Diffusion for Reference-Driven Image Completion
Venue: NeurIPS 2025
Accepted: yes
Keywords: Reference-Driven Image Completion
Abstract: Reference-driven image completion, which restores missing regions in a target view using additional images, is particularly challenging when the target view differs significantly from the references. Existing generative methods rely solely on diffusion priors and, without geometric cues such as camera pose or depth, often produce misaligned or implausible content. We propose GeoComplete, a novel framework that incorporates explicit 3D structural guidance to enforce geometric consistency in the completed regions, setting it apart from prior image-only approaches. GeoComplete introduces two key ideas: conditioning the diffusion process on projected point clouds to infuse geometric information, and applying target-aware masking to guide the model toward relevant reference cues. The framework features a dual-branch diffusion architecture. One branch synthesizes the missing regions from the masked target, while the other extracts geometric features from the projected point cloud. Joint self-attention across branches ensures coherent and accurate completion. To address regions visible in references but absent in the target, we project the target view into each reference to detect occluded areas, which are then masked during training. This target-aware masking directs the model to focus on useful cues, enhancing performance in difficult scenarios. By integrating a geometry-aware dual-branch diffusion architecture with a target-aware masking strategy, GeoComplete offers a unified and robust solution for geometry-conditioned image completion. Experiments show that GeoComplete achieves a 17.1% PSNR improvement over state-of-the-art methods, significantly boosting geometric accuracy while maintaining high visual quality.

GeoComplete: Geometry-Aware Diffusion for Reference-Driven Image Completion

Abstract

Reference-driven image completion, which restores missing regions in a target view using additional images, is particularly challenging when the target view differs significantly from the references. Existing generative methods rely solely on diffusion priors and, without geometric cues such as camera pose or depth, often produce misaligned or implausible content. We propose GeoComplete , a novel framework that incorporates explicit 3D structural guidance to enforce geometric consistency in the completed regions, setting it apart from prior image-only approaches. GeoComplete introduces two key ideas: conditioning the diffusion process on projected point clouds to infuse geometric information, and applying target-aware masking to guide the model toward relevant reference cues. The framework features a dual-branch diffusion architecture. One branch synthesizes the missing regions from the masked target, while the other extracts geometric features from the projected point cloud. Joint self-attention across branches ensures coherent and accurate completion. To address regions visible in references but absent in the target, we project the target view into each reference to detect occluded areas, which are then masked during training. This target-aware masking directs the model to focus on useful cues, enhancing performance in difficult scenarios. By integrating a geometry-aware dual-branch diffusion architecture with a target-aware masking strategy, GeoComplete offers a unified and robust solution for geometry-conditioned image completion. Experiments show that GeoComplete achieves a 17.1% PSNR improvement over state-of-the-art methods, significantly boosting geometric accuracy while maintaining high visual quality.

1 Introduction

Reference-driven image completion restores missing regions in a target image using additional views of the same scene. However, variations in viewpoint, occlusions, dynamic content, and camera settings make it difficult to identify and transfer useful information, posing significant challenges for accurate completion.

To address these challenges, traditional geometry-based methods [35, 48, 49] rely on a sequential pipeline of pose estimation, depth reconstruction, 3D warping, patch fusion, and image harmonization. However, as highlighted in [40], this approach is fragile, as early-stage errors often cascade and lead to failure in complex scenes with occlusions, dynamic content, or ambiguous geometry. To handle complex scenes, generative methods like RealFill [40] fine-tune diffusion models on masked reference images to directly synthesize missing regions. While effective, RealFill struggles when the target view differs significantly from the references. Without geometric cues like camera poses or depth, it often produces hallucinated structures or misaligned completions.

In this paper, we propose GeoComplete, a geometry-aware image completion framework that synthesizes missing regions with strong geometric consistency. GeoComplete is based on two key ideas: (1)

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injecting explicit geometric cues into a diffusion model by conditioning on the projected point cloud, and (2) guiding the model to focus on informative reference regions via target-aware masking. We define 'informative regions' as areas that are visible in reference views but missing from the target.

To obtain point clouds, our framework integrates two components: Visual Geometry Grounded Transformer (VGGT) [41] and Language Segment Anything (LangSAM) [25, 22, 32]. Unlike traditional geometry-based methods (e.g., [35, 48, 49]) that rely on sequential estimation steps, VGGT predicts key 3D attributes in a single forward pass. Trained on large-scale data, VGGT delivers accurate and efficient geometry estimation, even in complex scenes, though its performance may degrade with dynamic objects. To address this, we integrate LangSAM, which segments dynamic regions using text prompts. By filtering out moving content before point cloud generation, LangSAM enhances the robustness of geometry estimation. Prompts can be provided manually or generated automatically by a large language model (LLM) [1].

The resulting point cloud is projected to the target view and fed into our dual-branch diffusion framework, comprising a target branch and a cloud branch. The target branch encodes the masked image to generate missing content. The cloud branch processes the projected point cloud to provide geometric guidance. Joint self-attention fuses the two branches, enabling geometry-aware synthesis of missing regions.

To address the challenge of completing regions not visible in the target view, we introduce targetaware masking to guide the model toward useful and non-redundant reference cues. Using 3D attributes from VGGT, we project the target view into each reference to identify informative regions. Rather than masking reference images randomly as in RealFill [40], we selectively mask these informative regions to encourage the model to learn from content that complements the target view.

Figure 1 shows that GeoComplete significantly outperforms state-of-the-art methods, producing missing regions with strong geometric consistency. The main contributions of this work are:

Dual-branch Diffusion: We propose a geometry-aware dual-branch diffusion model that synthesizes missing regions with strong geometric consistency. It comprises a target branch, which conditions the diffusion model on the masked image to generate missing content, and a cloud branch, which conditions it on the projected point cloud to provide geometric cues.

Target-aware Masking Strategy: Unlike RealFill, which applies random masking to the reference image, our method selectively masks informative regions to guide the diffusion model toward meaningful cues, leading to more accurate and coherent completions.

Extensive Experiments: GeoComplete significantly outperforms existing methods in both structural accuracy and visual fidelity. Specifically, our method surpasses state-of-the-art approaches by 17.1% in PSNR on benchmark datasets.

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2 Related Work

Image Completion Traditional methods [15, 21, 38, 18] employ task-specific networks to fill missing regions. Existing generative approaches [45, 10, 24, 9, 37, 29, 2, 7, 6, 42, 20] leverage pre-trained diffusion models to achieve strong image generation capabilities. Inspired by this, several methods [37, 29, 2] fine-tune diffusion models with prompt guidance for image completion. In our setting, text prompts fail to capture the rich cues available in reference images, leading to suboptimal results. Reference-driven methods [49, 48, 35] combine depth and pose estimation, image warping, and harmonization, but these components are error-prone and often compound failures, especially in dynamic scenes. Moreover, their limited generative ability hinders plausible content synthesis. Recent diffusion-based methods [44, 40] draw on Stable Diffusion priors. Paint-by-Example [44] uses the target image and a CLIP embedding [28] of a single reference for semantic guidance, while RealFill [40] adapts the diffusion model per scene via LoRA to reconstruct masked references with multiple inputs. However, both approaches neglect geometric cues such as depth and pose, which are crucial for spatial consistency across views. Our method addresses this gap by explicitly injecting geometry into the diffusion model, enabling geometry-aware generation with improved spatial alignment. Concurrently, other works [33, 36] couple NeRF [26] or 3DGS [17, 5] with diffusion for scene inpainting. For example, the Geometric-aware 3D Scene Inpainter [33] conditions diffusion on multi-view images and geometry to reconstruct 3D structure. These methods, however, assume shared geometry across views, limiting applicability in dynamic or varying conditions.

Geometric Information Estimation Existing geometry-based completion methods [49, 48, 35] depend on separate estimation modules such as camera pose [46, 16], monocular depth [30, 19, 14], and feature matching with robust fitting [34, 12, 3, 31, 4] to enable view warping. In contrast, the Visual Geometry Grounded Transformer (VGGT) [41] unifies these tasks by jointly predicting camera parameters, depth maps, point maps, and 3D tracks directly from input views. While VGGT achieves strong results in static scenes, it struggles with dynamic objects. To address this, we incorporate LangSAM [25, 22, 32] to filter dynamic content before applying VGGT, enabling more reliable 3D attribute prediction in such settings.

3 Proposed Method

Figure 2 shows the overall pipeline of GeoComplete , which comprises three key components: point cloud generation, dual-branch diffusion, and target-aware masking. The point cloud generation module estimates camera parameters and depth maps from the reference and target images, constructs a 3D point cloud, and projects it onto both views to provide geometric guidance. The dual-branch diffusion model then synthesizes the missing regions while integrating this geometric information. Finally, the target-aware masking strategy directs the model to focus on reference regions that are not visible from the target view, encouraging the use of complementary cues.

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3.1 Point Cloud Generation

Figure 3 shows our point cloud generation pipeline. Given a set of reference images and a target image, we first obtain a text prompt that describes dynamic objects in the scene. The prompt is preferably provided by the user; if unavailable, it is automatically generated by a large language model (LLM)[1]. LangSAM[25, 22, 32] uses this prompt to segment and filter out dynamic regions in both the reference and target images. By removing dynamic objects, geometry estimation focuses on the static scene, enabling reliable correspondences across views.

We use VGGT [41] to jointly estimate camera parameters and depth maps from the filtered reference and target images, avoiding the error accumulation common in multi-stage geometry pipelines. The resulting 3D attributes form point clouds, which are projected onto both views to provide explicit geometric guidance. To prevent over-reliance on potentially inaccurate point clouds, we apply a conditional cloud masking strategy (Section 3.2) that introduces random masking during training.

Given a set of reference images { x ref i | i = 1 , 2 , . . . , N ref } and a target image x tar , we introduce a scene-specific text prompt p dyn to describe dynamic objects. If not provided by the user, p dyn is automatically generated by a large language model (LLM). Using this prompt, we apply LangSAM to segment dynamic regions in both reference and target images. The resulting segmentation masks are { m ref i } for the references and m tar for the target. We then mask out these dynamic regions to produce filtered images { ˜ x ref i } and ˜ x tar , preserving only the static content of the scene.

To estimate the camera parameters and depth maps for all filtered reference images and the target image, we formulate the prediction process using VGGT as:

where { c ref i } and { d ref i } are the predicted camera parameters and depth maps for the reference images, and c tar and d tar are those for the target image. θ vggt is the pre-trained parameters of VGGT.

To obtain the projected point cloud for each reference image x ref i , we first exclude its own information during point cloud construction. The resulting point cloud is then projected onto the reference view, formulated as:

̸

where π -1 ( · ) denotes the back-projection from depth maps to 3D space, and π ( · ) denotes the forward projection onto the 2D image plane. p ref i is the projected point cloud for the reference image x ref i . Similarly, the point cloud constructed from all reference images is projected onto the target view:

where p tar is the projected point cloud for the target image x tar .

3.2 Target-aware Masking

During training, we apply target-aware masking to selectively mask both the reference images and their projected point clouds. Using 3D geometric attributes from point cloud generation, we project the target image into each reference view to identify regions that are absent in the target (i.e., informative regions). As shown in Figure 2, these informative regions provide complementary information, while the remaining areas are treated as redundant cues.

We then apply two conditional masking strategies: conditional reference masking and conditional cloud masking. The reference masking randomly masks informative regions while preserving redundant ones, encouraging the model to learn from complementary content. The cloud masking, on the other hand, randomly applies white padding to the projected point maps while keeping informative regions intact, guiding the model to leverage geometric cues in these informative areas.

Given the predicted depth maps ( { d ref i } and d tar ) and camera parameters ( { c ref i } and c tar ), we project the filtered target image ˜ x tar onto each reference view ˜ x ref i , defined as:

where p tar → ref i is the projection of the target view into the i -th reference view. We convert this projection into a binary mask r ref i , where visible regions are set to 0 and zero-valued regions are set to 1.

The conditional reference masking is defined as:

where m rand i ∈ { 0 , 1 } H × W is a random binary mask applied only to the informative regions, and H and W are the height and width of the reference image. The operator ⊙ denotes element-wise multiplication, and ˆ x ref i is the resulting masked reference image. This operation preserves redundant content while randomly masking informative, non-redundant regions.

In contrast, conditional cloud masking retains non-redundant geometric regions while applying random masking to redundant ones. It is defined as:

where v fill is a predefined fill value assigned to masked-out points, and ˆ p ref i is the masked projected point cloud for the i -th reference view. This operation guides the model to rely on geometric cues in regions where visual reference content is lacking.

Following the training strategy of [40], we also sample the target image x tar as input during training. We apply random masking to obtain ˆ x tar , while the projected point cloud p tar is directly used as ˆ p tar .

3.3 Dual-branch Diffusion

Figure 2 shows the pipeline of our dual-branch diffusion model, which consists of a target branch and a cloud branch. The target branch conditions the diffusion model on a masked image to generate the missing regions. The cloud branch conditions it on a projected point cloud to provide geometric cues. The masked image and projected point cloud are first encoded into the latent space using a VAE encoder. The resulting latent features, along with a noisy latent, are then passed to a UNet for denoising.

To enable information exchange between branches, we concatenate hidden features from the target and cloud branches before computing self-attention. This design allows the model to adaptively integrate visual and geometric cues. However, since most regions in the target image are masked, the resulting latent features often lack meaningful information. Although these masked tokens can attend to the cloud branch, they struggle to extract useful guidance. To overcome this, we modify the attention mask to explicitly link each masked token in the target branch to its corresponding token in the cloud branch. This ensures that the target branch receives direct geometric cues, even when visual information is absent.

Given the hidden features from the target and cloud branches, denoted as h tar and h pt , we concatenate them along the token dimension:

where h cat ∈ R 2 L × d , with L representing the number of tokens per branch and d the feature dimension. The combined features h cat are then used for self-attention.

To control information flow, we introduce an attention mask m attn ∈ R 2 L × 2 L during self-attention. The mask is constructed to: (1) allow tokens within the same branch to attend to each other, (2) permit each target-branch token to attend to its corresponding cloud-branch token, and (3) block all other cross-branch interactions. An illustration of the attention mask is shown in Figure 2. The joint self-attention is formulated as:

where f self-attn ( · ) denotes the masked self-attention operation, and h attn is the resulting attended feature.

During training, the diffusion loss is defined as:

where L is the diffusion loss, B is the batch size, and ϵ θ ( · ) denotes the predicted noise. The conditional inputs satisfy ˆ x j ∈ { ˆ x ref i } ∪ { ˆ x tar } and ˆ p j ∈ { ˆ p ref i } ∪ { ˆ p tar } . Here, x j ( t ) is the ground-truth image at timestep t , and w j is a weighting map indicating valid regions (e.g., visible areas in the target view). The loss is computed only over these valid regions.

During inference, we use p tar and x tar as conditional inputs to guide the dual-branch diffusion, generating missing regions while preserving geometric structures.

4 Experiments

In our experiments, we follow the evaluation protocol of [40] and test on two challenging referencebased image completion datasets: RealBench and QualBench.

RealBench [40] contains 33 scenes (23 outpainting and 10 inpainting). Each scene provides 1-5 reference images, a target image with missing regions, a binary mask, and a ground-truth completion. Scenes include large variations between target and references, such as viewpoint, blur, lighting, style, and pose. Evaluation uses six metrics: PSNR, SSIM, LPIPS [47], DreamSim [13], DINO [8], and CLIP [28]. PSNR, SSIM, and LPIPS capture low-level quality, while DreamSim, DINO, and CLIP assess perceptual fidelity at mid- and high-levels.

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QualBench [40] includes 25 scenes collected in the same way but without ground-truth completions. We therefore conduct a user study where participants rate each result (1-5) based on: (1) realism of the restored content, (2) consistency with references, and (3) structural and color coherence with the unmasked target. Higher scores reflect more natural, geometrically consistent, and visually coherent completions.

4.1 Implementation Details

All experiments are conducted on a server equipped with four NVIDIA GPUs, each with 24 GB of memory. Our implementation involves three key components: point cloud generation, target-aware masking, and dual-branch diffusion, each of which is described in detail below.

Point Cloud Generation Our point cloud generation pipeline incorporates two key components: LangSAM [25, 22, 32] and VGGT [41]. In LangSAM, we employ SAM 2.1-Large [32] for segmentation. The text prompts are either manually provided by users or automatically generated by a large language model, ChatGPT-4o [27]. Since VGGT only supports inputs of size 518 × 518 , we resize the reference and target images while preserving their aspect ratios. After resizing, a center crop is applied to obtain the final 518 × 518 resolution.

Target-aware Masking Our target-aware masking consists of a conditional reference masking and a conditional point masking. Following the strategy in [38, 39], the conditional reference masking first generates multiple random rectangles and constructs the initial mask by either taking their union or the complement of their union. Subsequently, following Equation 5, it selectively unmasks less informative regions in the reference images. Similarly, the conditional point masking first generates the initial mask and then selectively unmasks non-redundant geometric regions, as defined in Equation 6. The fill value v fill is set to 1 (white) to replace masked-out regions in the projected point cloud.

Dual-branch Diffusion Our diffusion model is built upon Stable Diffusion 2 Inpainting [37]. We fine-tune it with LoRA, updating only rank-decomposed layers in the U-Net while keeping original weights frozen. The LoRA rank is set to 8 to balance adaptation capacity and training efficiency. For each scene, we fine-tune the model for 2,000 iterations with a batch size of 16. During training, all reference and target images are resized to a resolution of 512 × 512 .

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4.2 Evaluation

Quantitative Table 1 compares GeoComplete with state-of-the-art methods on RealBench and QualBench. Baselines include prompt-based approaches (SD Inpaint [37], Generative Fill [2]) and reference-based methods (Paint-by-Example [44], TransFill [49], RealFill [40]). The prompt-based models rely on text input, while the reference-based ones use images for completion.

Compared to prompt-based methods, GeoComplete achieves large gains across all low-level metrics, improving PSNR by over 5 dB and reducing LPIPS from 0.605 to 0.237. Against TransFill, a geometry-aware baseline, our model benefits from VGGT and LangSAM to generate more reliable point clouds, yielding notable improvements. While RealFill already performs strongly with masked reference conditioning, GeoComplete further improves SSIM (0.424 → 0.555) and reduces LPIPS by 0.194, producing sharper and more perceptually faithful reconstructions. These results underscore the importance of explicit 3D geometric priors and validate the effectiveness of our dual-branch diffusion design.

Qualitative Figures 4 and 5 show qualitative comparisons on RealBench. Generative frameworks such as RealFill leverage reference images to produce plausible completions, but without explicit geometry they often fail to maintain spatial consistency, leading to misaligned or implausible content. In contrast, GeoComplete enforces geometric consistency by integrating priors from LangSAM and VGGT within a dual-branch architecture that jointly encodes visual and 3D cues. As illustrated in Figure 4, GeoComplete reconstructs fine details and preserves scene-level alignment, even under large viewpoint changes between the target and references.

4.3 Ablation Studies

We conduct ablation experiments to evaluate the contributions of target-aware masking and dualbranch diffusion. Results are reported in Table 2. Without geometric guidance, our method reduces to RealFill, shown in the first row as the baseline.

Dual-branch Diffusion We compare GeoComplete with and without explicit geometric cues. As shown in Table 2, removing geometry causes clear drops across all metrics (e.g., PSNR and SSIM decrease by 1.59 and 0.131). Figures 4 and 5 further illustrate that without geometry, RealFill often produces hallucinated or misaligned content. By contrast, GeoComplete integrates geometric information into the generation process, yielding structurally consistent results. This highlights the importance of explicit geometry in guiding diffusion-based restoration.

Joint Self-Attention We ablate the joint self-attention module, which fuses target and cloud-branch features under a controlled attention mask. As shown in Table 2, removing this module results in noticeable drops in PSNR, CLIP, and DINO, reflecting weaker low-level fidelity and high-level semantic alignment. These results demonstrate the role of joint self-attention in ensuring alignment between the target and projected point cloud.

Target-aware Masking We also evaluate the effect of target-aware masking. Removing this strategy consistently reduces performance across all metrics (e.g., PSNR and SSIM drop by 0.47 and 0.014, while CLIP and DINO decrease by 0.003). This indicates that target-aware masking helps the model focus on non-redundant regions in the references, improving inference accuracy and fidelity.

Robustness of GeoComplete GeoComplete relies on upstream modules such as VGGT and LangSAM, whose outputs may contain errors (see Sec. 3.1). To mitigate this, we introduce conditional cloud masking (CM), which prevents the model from over-relying on unreliable geometry. We also employ joint self-attention with masking (JSA), which enforces token-to-token links between the target and cloud branches, ensuring that noisy cloud tokens do not propagate globally through cross-attention, particularly when they dominate.

To evaluate robustness, we simulate (1) noisy point clouds, (2) sparse point clouds, and (3) LangSAM segmentation errors. Details can be found in the Appendix. As shown in Table 3, GeoComplete shows only small drops under these perturbations and consistently outperforms both RealFill and the variant without CM and JSA. These results demonstrate the effectiveness of CM and JSA in maintaining strong performance even when upstream predictions are noisy or partially erroneous.

5 Conclusion

We introduced GeoComplete , a geometry-guided diffusion framework for reference-driven image completion. Unlike existing generative methods that operate solely in the image domain, GeoComplete incorporates explicit 3D geometry by conditioning the diffusion model on projected point clouds. To guide the model toward meaningful reference cues, we propose a target-aware masking strategy that filters redundant content and emphasizes complementary regions. Our dual-branch architecture jointly processes geometric and visual tokens through self-attention, enabling the synthesis of structurally accurate and visually coherent results. Extensive experiments on real-world and synthetic benchmarks demonstrate that GeoComplete achieves clear improvements over state-of-the-art methods in both geometric consistency and perceptual quality.

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Appendix

A Implementation Details

A.1 Detailed Clarification of Workflow

Initially, we process all reference images and the target image using VGGT and LangSAM to obtain the point cloud.

During training, given a reference image I ref and its corresponding projected point cloud I cloud , we use our conditional reference masking to mask the reference image, obtaining the augmented reference image I aug -ref ∈ R 3 × H × W and the mask image I mask ∈ R 1 × H × W . We also use our conditional cloud masking to augment the projected point cloud, resulting in I aug -cloud ∈ R 3 × H × W . Wethen encode these images into the latent space using a V AE. This results in a latent reference image I latent ref (used as the ground truth), a latent masked reference I latent aug -ref , and a latent projected point cloud I latent aug -cloud , all in R 4 × h × w , where h = H/ 8 and w = W/ 8 . The mask is also downsampled to I latent mask ∈ R 1 × h × w .

To fine-tune the diffusion model, we add noise to the ground truth latent I latent ref to obtain a noisy latent I latent noisy . The input to the target branch is the concatenation of I latent noisy , I latent mask , and I latent aug -ref , resulting in a tensor of shape R 9 × h × w . Similarly, the input to the cloud branch is the concatenation of I latent noisy , I latent mask , and I latent aug -cloud , also in R 9 × h × w . The objective is to estimate the added noise, which has shape R 4 × h × w .

During inference, given the target image I tar , its corresponding projected point cloud I cloud , and the mask image I mask , we directly process them into the latent space. This results in a latent target I latent tar and a latent projected point cloud I latent cloud . The mask is also downsampled to I latent mask . We initialize I latent noisy using standard Gaussian noise. Then, we concatenate the corresponding latent tensors to construct the inputs for the target and cloud branches. After an iterative denoising process and using the VAE decoder, we obtain the final output.

A.2 Details of Baseline Methods

For SD Inpaint [37] and Generative Fill [2], we follow the instructions from RealFill to generate long descriptions for each scene with the help of ChatGPT. For RealFill [40], we follow their official setting by fixing the text prompt to a sentence containing a rare token, i.e., 'a photo of [V]'. For a fair comparison, our method also adopts this setting.

A.3 Robustness Evaluation Details

To evaluate robustness, we simulate three conditions that introduce errors from VGGT and LangSAM:

Noisy Point Cloud: Gaussian noise is added to a subset of points in the generated 3D point cloud to mimic degraded geometry.

Sparse Point Cloud: A ratio of points is randomly dropped from the 3D point cloud before projection to simulate sparse geometry.

Segmentation Errors: Wemanually selected 13 RealBench scenes with significant dynamic objects and tested two variants: (1) removing LangSAM masks entirely and (2) introducing errors by randomly adding 10% extra masked regions per mask.

A.4 Prompt Design

Since dynamic objects can significantly affect the geometric predictions of VGGT [41], we introduce LangSAM [25, 22, 32] to filter out dynamic content before applying VGGT, thereby enabling robust 3D attribute prediction even in dynamic scenes. Prompts can be provided manually or generated automatically by a large language model (LLM) [1].

When using an LLM, we upload all reference images along with the target image. The following guided prompt is used:

Identify and list only the objects that are inconsistent across the images, such as dynamic objects that change position, appearance, or are missing. Ignore consistent background objects even if the viewpoint changes slightly.

B Comparison with Reference-Guided Image Editing Methods

In this section, we evaluate existing reference-guided image editing methods, including OmniGen [43], Step1x-Edit [23], and Bagel [11]. We initially follow the official instructions to run these baseline models. For scenes where the models fail to perform adequately, we employ ChatGPT to generate prompts and manually refine them as needed. However, these methods fail to handle all scenes. In some cases, the restored results become completely white or visually meaningless. In summary, only 28 scenes from OmniGen, 13 scenes from Step1X-Edit, and 28 scenes from Bagel produce valid outputs. PSNR and SSIM are computed on these successfully restored results, as summarized in Table 4. Overall, the results suggest that under the reference-based image completion setting, existing reference-guided image generation methods still perform suboptimally.

C Computational Cost

Table 5 summarizes the computational cost (using four 24G GPUs) of Paint-by-Example [44], RealFill [40], and our method. Both RealFill and our method are based on per-scene optimization. For a fair comparison, we adopt identical experimental settings, including batch size, number of optimization steps, and number of GPUs. Although our approach introduces slightly higher overhead than RealFill, it achieves significantly better reconstruction quality. Notably, our method reaches promising results within 500 steps (18 mins), outperforming RealFill even at 2000 steps (50 mins).

Compared to one-shot models such as Paint-by-Example, per-scene optimization methods generally provide more accurate content restoration but are less suitable for time-sensitive or real-time applications. As shown in Figure 4 and Figure 5, our method produces more faithful results, while Paint-by-Example often fails to preserve fine-grained content from the reference images.

To explore potential acceleration strategies, we first identify that the primary computational bottleneck in our framework lies in the 2,000-step per-scene fine-tuning process. One potential solution is to pre-train the LoRA parameters of the diffusion model on a large-scale, task-specific dataset. This would serve as a strong initialization for subsequent per-scene adaptation, thereby significantly reducing the number of required optimization steps while preserving the quality of generated results. We consider this an important direction for future work.

D Limitations

While GeoComplete effectively leverages geometric cues for reference-driven image completion, it inherits certain limitations from its components. For example, the quality of the projected point cloud depends on the accuracy of the geometry estimation module (e.g., VGGT [41]). To mitigate the impact of inaccurate point clouds, we introduce a conditional cloud masking strategy that prevents

the model from relying on unreliable geometric input. This allows our framework to generate realistic results even when the point cloud is inaccurate. However, when the point cloud is imprecise, the framework may not be able to fully exploit geometric information, which can affect completion quality in those regions.

E Societal Impact

Reference-driven image completion can benefit various applications, including occlusion removal, image editing, and scene understanding in both consumer and industrial domains. GeoComplete introduces explicit geometric information to guide the completion process, reducing hallucination risks and improving structural fidelity. However, as the framework relies on generative models to synthesize missing content, it may still produce plausible yet inaccurate completions, especially in regions with limited geometric or visual cues. Therefore, we recommend caution when applying such methods in safety-critical or forensic contexts that require guaranteed factual accuracy.

Peer reviews:
comments: SUMMARY:
This paper introduces a reference-guided image outpainting method by introducting feed-forward 3D foundation models to faciliate 2D tasks. It predicts geometric-aware outpainted regions through conditioning on the a 3D point cloud, and finetunes or use off-the-shelf diffusion models to fill in the missing region. It is an interesting and reasonable idea in itself that has demonstrated good results over the baseline. The evaluation on quality is sufficient.

STRENGTHS AND WEAKNESSES:
In general the idea makes sense, as filling in a 2d capture of a 3d scene with the reference from the 3D content will naturally outperform pure 2d based models in terms of the persective of the surroundings. My only concern is the runtime overload this method, which I believe should be heavier than 2D based methods which only requires model finetuning. It would be nice if the author can acknowledge this in the limitation or provide justification otherwise.

QUESTIONS:
How would a language model at all be able to tell what is the dynamic region of the image? I don't think the paper clearly defines it either. Considering the example on the first page, the cat was not in the reference images, how is it considered by the language model then? 

How is the runtime of this model compare to baseline and especially RealFill?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-25

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a new method for image completion (inpainting, outpainting, etc.) given (a) an image to be completed and (b) several other reference views of the scene, from different viewpoints. This longstanding problem in computer vision has been worked on even since before the days of DNNs, and work continues into the diffusion age. This paper suggests using a state-of-the-art point cloud method to do (sparse) novel viewpoint generation from the reference images, then trains an image completer that can consume these projected pointclouds. They make experimental comparisons to RealFill and Paint-By-Example.\n\nSTRENGTHS AND WEAKNESSES:\nQuality: Qualitatively and quantitatively the results are convincing. It seems that TransFill is a more apt comparison than PBE, especially given the paper’s claims about the superiority of one-shot geometry (lines 27-30), so I would prefer for the paper to include some qualitative comparisons here, especially ones that show clearly why pointclouds are a better 3D representation than multiple homographies.\n\nClarity: the paper is fairly readable and appropriately concise. This reviewer was a little bit confused about how the conditioning was added to the diffusion model; it looks at first like the conditioning itself has noise added; it’s hard to see how this could work at inference time since the noise, at least in traditional diffusion levels, would destroy all the conditioning signal! At first I thought it might be some kind of CFM variant  – but now I think the latents of the conditioning are concatenated onto the noise (lines 167-168).\n\nOriginality and significance: this is a novel approach that seems to improve the SOTA on a long-standing and well-established vision problem.\n\nI note that the authors promise to release code and training scripts, which is great from a reproducibility standpoint.\n\n(edited to add:) the authors may also be interested in comparing with this work: https://arxiv.org/pdf/2502.13335 (I believe the work to be roughly simultaneous).\n\nQUESTIONS:\nHow do comparisons with Transfill work?\n\nHow does the model degrade, as compared to other methods' degradation, as there is less overlap, i.e., the pointcloud covers less and less of the image and the model is forced to do more and more whole-cloth extrapolation?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents GeoDiff, a framework for reference-driven image completion that explicitly leverages 3D geometry to guide a diffusion-based generation process. The work targets a key limitation of existing generative methods: when there is a significant viewpoint discrepancy between the reference and target views, current approaches often yield geometrically inconsistent or misaligned results due to the absence of explicit 3D priors. The proposed method demonstrates impressive visual quality and delivers substantial improvements over prior state-of-the-art baselines, both quantitatively and qualitatively.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. Novelty and Significance. A key strength of the paper lies in its novel integration of explicit 3D geometric priors into a diffusion-based framework for reference-guided image completion. This design directly addresses a major limitation of prior generative approaches—namely, the inability to maintain geometric consistency under large viewpoint changes—and achieves demonstrable improvements in geometric fidelity.\n\n2. Good results. The paper conducts comprehensive empirical validation, including both standard quantitative metrics (PSNR, SSIM, LPIPS, etc.) and a user study for qualitative assessment. The ablation studies are well-designed and effectively highlight the individual contributions of each model component.\n\n**Weaknesses**\n1. Dependency on Upstream Modules. The framework's reliance on external modules such as LangSAM (for segmentation) and VGGT (for depth and pose estimation) introduces a significant vulnerability. Inaccuracies in these modules can propagate through the pipeline and compromise the quality of geometric guidance, ultimately degrading the final output. However, the paper does not sufficiently analyze or quantify these potential failure modes.\n\n2. High Computational Cost.The proposed approach requires per-scene fine-tuning of the diffusion model over 2,000 iterations, alongside the overhead of running LangSAM and VGGT. This makes the method computationally demanding and limits its applicability in time-sensitive or real-time settings. A discussion on scalability or potential acceleration strategies would be beneficial.\n\n3. Limited Handling of Dynamic and Complex Scenes. The reliance on text-based prompts in LangSAM introduces limitations in dynamic or cluttered environments, especially when accurate or complete textual descriptions are unavailable. This restricts the method’s generalizability to complex, real-world scenes with multiple or moving objects.\n\n4. Lack of Comparison with Stronger Baselines. The experimental section omits comparison with several recent multi-modal generation methods, such as OmniGen[1], Step1x-Edit[2], and Bagel[3]. Including such baselines would provide a more complete picture of the method's relative advantages and limitations, especially since these models also support reference-conditioned generation in diverse settings.\n\nReferences:\n- [1] OmniGen: Unified Image Generation\n- [2] Step1X-Edit: A Practical Framework for General Image Editing\n- [3] Unified Model for Multimodal Understanding and Generation\n\nQUESTIONS:\nWhile the paper presents promising results, I still have a few unresolved concerns. I would be willing to re-evaluate my score upon receiving satisfactory clarification from the authors.\n1. How robust is the method to errors from upstream modules such as LangSAM and VGGT?\nThe proposed pipeline depends on accurate segmentation and depth/pose estimation from external models. Have the authors evaluated how errors in these stages (e.g., incorrect masks or noisy point clouds) affect the final image completion results? Are there any mechanisms to mitigate error propagation?\n2. Can the authors elaborate on the computational cost and scalability of the approach?\nGiven that the method requires 2,000 iterations of per-scene fine-tuning in addition to inference from LangSAM and VGGT, how practical is the framework for large-scale or real-time applications? Have the authors explored any strategies for reducing the computational burden?\n3. Models such as OmniGen, Step1x-Edit, and Bagel also support reference-guided image generation and could serve as strong baselines. Could the authors clarify the rationale behind the choice of baselines, and possibly include comparisons with these more recent methods?\n\nLIMITATIONS:\nPlease refer Questions for details.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GeoDiff, a framework for reference-driven image completion, a task that aims to fill missing regions in a target image using information from other views of the same scene. The authors identify a critical weakness in existing state-of-the-art generative methods, such as RealFill, which, despite their powerful image priors, often produce geometrically inconsistent or misaligned results due to a lack of explicit 3D awareness. GeoDiff proposes to remedy this by directly integrating 3D geometric information into the generative process. Empirically, the authors demonstrate substantial improvements over existing methods on the RealBench and QualBench datasets (which are also introduced in this paper).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n * The paper tackles an important problem. Methods like RealFill have pushed the state of the art by personalizing diffusion models on a few reference images, but their reliance on a 2D image prior without geometric understanding often leads to structural failures, hallucinations, and misalignments when there are significant viewpoint changes between the reference and target images. GeoDiff's central goal of enforcing explicit 3D geometric consistency is therefore not just an incremental improvement but a necessary and logical next step for building more robust, reliable, and authentic image completion tools. Solving this problem has direct applications in computational photography, virtual reality, and digital content creation.\n\n* Well-motivated technical approach that leverages VGGT and Lang-SAM pre-trained models. \n\n* Compelling empirical evaluation -- the paper presents a comprehensive set of experiments that, on the surface, convincingly demonstrate the method's effectiveness.\n\nWeaknesses:\n * Incomplete literature review. The failure to cite and discuss these works is not a minor oversight. It creates a cascade of problems: it allows for an inflated and inaccurate novelty claim, it prevents a meaningful comparison against the true state-of-the-art in geometry-aware generative inpainting, and it ultimately weakens the perceived contribution of the paper. Some important omissions include: \n   *  GeoFill (WACV 2023), explicitly estimates a 3D mesh and relative camera pose from two views to guide reference-based inpainting. While it does not use a diffusion model for the final synthesis, it establishes a strong and direct precedent for leveraging explicit 3D scene representation for this task.\n   * Geometry-Aware Diffusion Models for Multiview Scene Inpainting. It introduces a \"geometry-aware conditional diffusion model\" that uses projected geometric and appearance cues from reference images to perform multi-view consistent inpainting.\n   * OccludeNeRF focuses on 3D NeRF inpainting by guiding a diffusion model with collaborative score distillation. It explicitly reasons about geometry and occlusions to achieve 3D consistent results, representing another instance of combining 3D scene representations with diffusion models for completion tasks.\n\n* QualBench & RealBench - are not standard benchmarks. The paper introduces these two datasets, instead of re-using a dataset from a prior work, e.g., RealFill has one available. Generally, it's not a good idea to evaluate a new method on a new (not a well established) dataset.\n\nQUESTIONS:\n* Given the existence of several peer-reviewed works on geometry-aware generative in-painting from 2022-2025, could you please clarify and refine your paper's core novelty claim beyond being the \"first\" to couple geometry and diffusion for this task?\n\n* The framework relies heavily on the output of VGGT. Could you discuss the robustness of GeoDiff to potential failures in the geometry estimation stage? Specifically, how does the model perform when provided with a noisy or inaccurate point cloud, and have you considered adding an ablation study to demonstrate this?\n\n* Could you please expand on the evaluation setting for the baseline methods (e.g., how were the prompts chosen for SD, RealFill, etc)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-07"}