Title: FlexWorld: Progressively Expanding 3D Scenes for Flexible-View Exploration
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D scene generation

Diffusion model

3D Gaussian Splatting
Abstract: Generating flexible-view 3D scenes, including 360° rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework that progressively constructs a persistent 3D Gaussian splatting representation by synthesizing and integrating new 3D content. To handle novel view synthesis under large camera variations, we leverage an advanced pre-trained video model fine-tuned on accurate depth-estimated training pairs. By combining geometry-aware scene integration and optimization, FlexWorld refines the scene representation, producing visually consistent 3D scenes with flexible viewpoints. Extensive experiments demonstrate the effectiveness of FlexWorld in generating high-quality novel view videos and flexible-view 3D scenes from single images, achieving superior visual quality under multiple popular metrics and datasets compared to existing state-of-the-art methods. Additionally, FlexWorld supports extrapolating from existing 3D scenes, further extending its applicability. Qualitatively, we highlight that FlexWorld can generate high-fidelity scenes that enable 360° rotations and zooming exploration. Our code is available at https://github.com/ML-GSAI/FlexWorld.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a pipeline for generating a complete 3D scene from a single image. The core component is a video diffusion model trained on a carefully curated dataset designed to ensure view consistency. Based on this model, the authors employ 3D Gaussian Splatting (3DGS) to progressively fuse information from the diffusion model in a geometry-consistent manner.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* The proposed dataset construction pipeline helps improve the quality of training data and subsequently enhances the performance of the video diffusion model. \n* Qualitative results show that the presented diffusion model achieves better view consistency compared to prior work.\n\nConcerns:\n* While the results look promising, I find some parts of the writing unclear and a bit hard to follow. In particular, many sentences are overly long and could be made more concise. Additionally, reiterating the problem motivation in the Method section disrupts the flow and makes the technical content harder to follow, for example, Ln 117–119 and 127–129 could be more compact. At the beginning of Section 3.2, figure 2 can be used to help explain the overall pipeline. Currently figure 2 is not referenced in the text. Additional clarity-related questions are listed below.\n* The authors claim that the proposed method allows flexible camera control, however, this is not clearly supported by the experiments, as there is no analysis on the effect of different camera trajectories. \n* It appears that the improvement in the video diffusion model primarily stems from the proposed dataset generation pipeline. Since this is one of the main contributions of the paper, it would be helpful to include more in-depth discussion and experimental analysis of this dataset generation pipeline in the main paper.\n\nQUESTIONS:\n* Ln 122 reads like the proposed V2V model takes camera trajectory as input, but from Ln 164, camera trajectory c is only used for rendering videos with missing geometry y, is this correct? \n* Is the camera trajectory mentioned in Line 124 the only trajectory used across all experiments?\n* Is Figure 4 the output of the proposed V2V model or from the optimized 3DGS? I find the term “novel view synthesis” confusing, maybe specify if the video is rendered from 3DGS or generated with the diffusion model.\n* How is y obtained in Line 173? Is it rendered from the optimized 3DGS? If so, how do you ensure that y contains missing information or artifacts? My understanding is that y and x share the same camera trajectory from the original dataset, and therefore y should be very similar to x.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFlexWorld is a framework for generating flexible-view 3D scene from a single input image. It maintains a persistent 3DGS representation that is progressively expanded: a video-to-video (V2V) diffusion model synthesizes novel view videos from incomplete renders, new 3D content is extracted via dense stereo depth estimation and integrated into the scene, and the combined representation is jointly optimized for visual and geometric fidelity. \nBy fine-tuning a video model on high-quality depth-aligned training pairs, FlexWorld achieves consistent novel view synthesis under large camera variations. Extensive experiments on RealEstate10K, Tanks-and-Temples, and DL3DV-10K demonstrate that FlexWorld outperforms prior video-based and image-based novel view synthesis methods in perceptual metrics camera accuracy, and single-image-to-3D rendering quality. FlexWorld also uniquely supports extrapolating existing 3D scenes into broader flexible-view environments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Maintains and refines a single 3DGS scene rather than rebuilding per iteration, enabling seamless expansion and scene extrapolation.\n\n2. Combines geometry-aware scene integration with 3DGS optimization losses and optional image-level refinement, resulting in both visual polish and geometric accuracy.\n\n3. Demonstrates superiority across multiple benchmarks and tasks—novel view synthesis, single-image 3D scene generation, and scene extrapolation—using a wide array of perceptual and geometric metrics.\n\nWeaknesses:\n1. Employing a video diffusion model to “paint” novel views based on point‐cloud renders is a well‐established approach in camera‐controlled video and 3D scene generation, so the core pipeline raises questions about its overall novelty.\n\n2. The manuscript does not convincingly demonstrate why a 3DGS representation outperforms traditional point‐cloud outputs which can be directly obtained from methods like DUSt3R or VGGT.\n\n3. Evaluation is centered on 2D metrics; adopting a 3D‐centric metric suite such as WorldScore would provide a more comprehensive assessment of camera‐controlled video generation and underlying scene quality.\n\n4. I also want to know the accumulated error during progressively expanding the scene.\n\nQUESTIONS:\nPlease refer to Weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFlexWorld presents a comprehensive framework for 3D scene synthesis under large view-point changes in 360 degrees. Powering FlexWorld is a fine-tuned V2V model and a progressive scene expansion and integration framework.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The methods are described clearly and experiments show high quality of work.\n- Strong quantitative results on both novel view synthesis and 3D scene generation on unseen test sets against baselines.\n- Strong qualitative results on novel view synthesis.\n- Proposed methods, including training data construction and generation framework, are principled and well motivated, with qualitative ablation on inclusion of video diffusion, zooming on camera trajectory, and refinement.\n\nWeaknesses \n\n- The paper mentions the stronger V2V model is mainly driven by better construction of training data (see Figure 6) and a vastly stronger base V2V model, CogVideoX-5B-I2V. However, the technical novelty behind this remain rather limited.\n- Full pipeline is rather complex, requiring v2V, Real-ESRGAN for super resolution, and FLUX for image refinement. Contributions of individual components are unclear.\n- Lack of quantitative ablation, specifically on refinement using a powerful image model, FLUX.1-dev, which may impact the quantitative comparisons.\n\nQUESTIONS:\n- What are the quantitative results for ablations in Figure 8? It would be good to see the quantitative results w/o FLUX refinement and after FLUX refinement. This would be especially helpful if metrics for strong baseline ViewCrafter is included.\n- How are unobserved regions (black regions in figure 6) from training dataset creation handled during V2V training?\n- Are there more details about any filtering done on DL3DV-10K dataset and the camera trajectories used to rendering video sequences?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes FlexWorld, a progressive framework for generating 3D scenes that support flexible-view exploration from single images. FlexWorld uses an accumulated 3D Gaussian splatting representation progressively expanded by synthesizing new 3D content via a fine-tuned video-to-video (V2V) diffusion model. Extensive evaluations demonstrate improvements over existing methods in terms of visual quality, viewpoint flexibility, and consistency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The motivation of the studied problem and the proposed method is clear.\n\n2. The proposed method achieves better results compared to existing methods on Rel10k and Tanks-and-Temples datasets. \n\n3. The proposed method is a system level integration of a custom V2V model, 3DGS buffer, and I2I refinement with  FLUX.1-dev.\n\n\nWeakness:\n1. Incremental novelty: The core technical novelty primarily relies on integrating existing concepts (3D Gaussian splatting and V2V diffusion). Although well-executed, individual components are incremental improvements over recent work like ViewCrafter or See3D. I appreciate the efforts in preparing incomplete-view datasets, i.e., training thousands of 3DGS on the DL3DV dataset to obtain paired training data, but I have to acknowledge the limited novelty of the overall framework.\n\n2. The proposed method is very computationally demanding, requiring 30 minutes for generating a single scene. This limits its broader applicability.\n\nQUESTIONS:\n1. Can the authors clearly explain the difference between the \"3D Scene Generation\" task and the \"Novel View Synthesis\" task in the context of this work? The current manuscript does not clearly differentiate these tasks. I don't find any difference.\n\n2. Could the authors provide the exact number of samples included in the training sets? Specifically, how many 3DGS scenes from DL3DV were successfully reconstructed and utilized during training?\n\n\n3. How much computation resource do you need to fit all Gaussians, apart from training the V2V model?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}