FlexWorld: Progressively Expanding 3D Scenes for Flexible-View Exploration
Authors: Luxi Chen
Zihan Zhou
Min Zhao
Yikai Wang
Ge Zhang
Wenhao Huang
Hao Sun
Ji-Rong Wen
Chongxuan Li
Venue: NeurIPS 2025 poster ()
Keywords: 3D scene generation
Diffusion model
3D Gaussian Splatting
Primary area: applications
TL;DR: FlexWorld generates flexible-view 3D scenes from single images using progressive expanding 3D Gaussian splatting and a fine-tuned  video-to-video model, outperforming existing methods in quality and exploration flexibility.

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.

Flex Progressively Expanding 3D Scenes for Flexible- Exploration
Luxi Chen 1,2,3, Zihan Zhou 1.2,3, Min Zhao', Yikai Wang", Ge Zhang
Wenhao Huang Hao Sun 1.2.3 Jii-ong Wen 12,33 Chongxuan Li Li
Gaoling School of Artificial Intelligence, Renmin University of China 2 Beijing Key Laboratory of Research on Large Models and Intelligent Governance
3 Engineering Research Center of Next- Gext-Generation Intelligent Search and Recommendation, MOE 4 Dept. of Comp. Sci. & Tech., BNRist Center, THU- Bouch MLCenter, Tsinghua University School of Artificial Intelligence, Beijing Normal University 6 ByteDance Seed
Abstract
Generating flexible-view 3D scenes, including 360 rotation and zooming, from single images is challenging due to lack of 3D data. To this end, introduce FlexWorld, a novel framework that progressively constructs a persistent 3D Gaus- sian splatting representation by synthesizing and integrating new 3D content. To handle novel view synthesis under large camera variations, we leverage an ad- vanced pre-trained video model fine-tuned accurate depth-estimated training pairs. By combining geometry-awant scene integration and optimization, Flex- World refines the scene representation, producing visually consistent 3D scenes with flexible viewpoints. Extensive experiments demonstrate the effectiveness of Flex World 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-an methods. Additionally, Flex World supports extrapolating from existing 3D scenes, further extending its applicability. Qualitatively, we highlight that forld can generate high-fidelity scenes that enable 360 rotations and zooming exploration. Our code is available at mttttps://githnni http:chwww..caaaLLAaAAAAA//eeeee
Introduction
Creating scene with flexible views from a single image holds transformative potential for applications where direct 3D data acquisition is costly or impractical, such archaeological preser- vation and autonomous navigation. However, this task remains fundamentally ill-posed: a single 2D observation provides insufficient information to disambiguate the complete 3D structure. In particular, when extrapolating to extreme viewpoints (c.g., 180 rotations), previously occluded or entirely absent content may emerge, introducing significant uncertainty.
Generative models, particularly diffusion models [ 3, offer a principled and effective solution to this problem. While existing methods often rely on pre- pre-trained generative models as priors for novel view synthesis, they face notable limitations. Image-based diffusion methods accumulate geometric errors, whereas video-based diffusion approaches & 9 struggle with dynamic content and poor camera supervision. Recent attempts [10 incorporate point cloud priors for
6 tend to
"Equal Contribution.
Coorespondence to Chongxuan (chongxuanli@rue edu. cn>, Yikai Wang <yikaiw@bnu, edu, en>
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Input
Videos generated by our V2V model given camera trajectories
3DGS
Vine Jeom
Viswi Zoum out
View 130
View JR0
View 22:52
View 315t
Zaom OUT
Vnew
(b) Input
Flexible-view 3D generated by Flex World
(c) Input
360 extrapolation from existing scenes using World
Figure 1: FlexWorld generates high-quality videos with camera control and flexible-view 3D scenes progressively. (a) Flex World introduces V2V diffusion producing high-quality videos from incomplete scene renderings given diverse camera trajectories with large variation. (b) FlexWorld World progressively generates flexible-views (e.g., 360 rotations and zooming) 3DGS scenes via V2V model. (c) Flex World further supports extrapolating existing scene into 360 exploration.
improved consistency have shown promise but remain limited in scalability, often failing under large viewpoint changes, further limiting the flexibility in exploring generated 3D scenes.
To this end, we propose Flex World for flexible-view 3D scene generation from single images. In contrast to existing methods [12 I3 [4] Flex World maintains a persistent 3D Gaussian splatting representation that is progressively expanded by synthesizing and integrating novel 3D content. To ensure robust novel view synthesis under large camera variations,
develop video-to-video (V2V) model with an advanced video foundation model [15) fine-tuned on accurate depth- setth-estimated training pairs. The V2V model generates complete view images from incomplete ones rendered from current scene. Through geometry-awart scene integration and optimization, FlexWorld progressively details the persistent scene representation, ultimately producing flexible-view 3D scenes with strong geometric and visual consistency from single images and extrapolating from existing scenes.

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FlexWorld
Derse stereo
Output
Flexible-view (e.g. 360 rotation) renderings
Progresiive 3D expansion
Update
Expansion
Integration
Rendaing
Dense stareo
Optimization
BF
arge view change
Strong V2V diffusion
Novel view synthesis
Figure 2: Overview Flex World. FlexWorld trains strong V2V diffusion capable of generating high-quality videos from incomplete views rendered from coarse 3D scenes. It progressively expands 3D scene by adding new 3D content estimated from the refined videos via scene integration. Ultimately, from single image, yields detailed 3D scene capable of rendering flexible viewpoints.
Our extensive experiments demonstrate World's effectiveness both high-quality video and flexible-view 3D scene synthesis. In particular, V2V model achieves superior visual quality compared to the current State-of-the-ans baselines 8 9 13
10 while maintaining excellent camera controllability across multiple benchmarks [66 17 (see Tab. D. A similar conclusion holds
for the 3D scene generation benchmarks (see Tab. 2 In addition, Flex World enables the synthesis of
the 3D scene with flexible view in high-fidelity and the extrapolation of existing DL3DV [18] scenes (see Fig. T into 360 exploration, consistent with our quantitative results.
In summary, key contributions are:
We propose Flex progressive framework for flexible-view 3D scene generation that builds and refines persistent scene representation by expanding it with synthesized novel views.
We introduce video-to- vidio diffusion model fine-tuned on optimized training data under an advanced base model, enabling consistent novel view synthesis under large camera variations.
Flex World exhibits superior performance in video and scene generation compared with baseline while supporting scene extrapolation.
models on various benchmark datasets [16 17
2 Related work
2.1 3D scene generation
With the emergence of 3D representations that enable differentiable rendering
19
20
3D object
generation from single texts/images has advanced rapidly [21] 22 23 24
25
26
27
28
29
closely
followed by advancements in 3D scene generation. Several works 4 30
5
3
32 employ image
diffusion models [33 34] for novel view synthesis and monocular depth estimation
35
36
37
to derive 3D structures for corresponding views. Another line of the work [39
40
41
involves training a network obtain 3D representation from single or sparse images directly. Recent studies have integrated camera control into image [46] 47 48 [I or video models [22
14
10
B3 49
to facilitate novel view synthesis, subsequently performing 3D reconstruction [50] 51 to obtain representations of 3D scenes. The method using video model can generate scenes with better consistency. However, constrained by large viewpoint changes in videos generated in single pass and the neglect of integrating generated videos into existing 3D scenes, these methods ultimately

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View 10
Scc3D i
Ours
View 45.0
View 52.5
View 165.0
View 172.5
Figure 3: We improve video diffusion model to enable generating 3D consistent videos under large camera variation. We present novel views generated from each model when the camera is rotated 180 degrees to the left. The red bounding box indicates 3D inconsistency poor visual quality in the generated content. Our model generates higher quality and more consistent 3D scenes.
result limited scene scale. Leveraging persistent 3DGS representation and strong V2V capable of handling large view variations, Flex World progressively expands the viewable area, ultimately enabling flexible-view 3D scenes generation and scene extrapolation.
2.2 Camera-controlled video diffusion models
Recently, Camera-controlled video diffusion models have received widespread attention. Several explore the generation of videos under camera conditions. However, these models are not designed for static scene generation, as the dynamics in the generated videos hinder reconstruction. DimensionX (13 achieves basic camera control via several LoRAs (55) but lacks flexibility in complex movements. Wonderland [2] and StarGen 14 can generate videos from single views and camera trajectories; however, they are unable produce new videos complement existing 3D structures, restricting the range of generated scenes. See3D [I] and ViewCrafter [10] can accept missing scene information from specific cameras and perform completion, but they struggle to accommodate significant viewpoint changes (see Fig. 3). In contrast, we propose training V2V model on a more advanced video foundation model, leveraging existing scene information to enable large camera variation and offering a powerful tool for flexible-view 3D scene generation.
works 8 9 52
3 Method
In this section, we will first introduce the preliminaries for FlexWorld. Subsequently, we will present flexible-view 3D scene generation framework in Sec. 3.2 where improved V2V model that
supports our framework will be further discussed Sec. 3.3
3.1 Preliminaries
Video diffusion model. A diffusion model [ 3 consists of forward and a denoising process. In the forward process, the diffusion model gradually adds Gaussian noise clean image zg from time 0 to T. The noisy image at certain time E [0. 7] can be expressed where o; and o; are predefined hyperparameters. In the denoising process, noise predictor ( t) with parameters 0 is trained to predict noise in æ, for generation. Given the corresponding condition y for x, the training objective a diffusion model is:
0,30 + aie,
min E 14--200.))))~000... [1leo(1e,
4; <112]
typically employ 3D-VAE 61 encoder & to compress the source video into latent space where the diffusion model is trained. The generated latent video is subsequently decoded to the pixel space using the corresponding decoder D.
Recent video diffusion models
56
57
58
15
59
60

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Dense stereo model. The dense stereo models 150 51 62 e.g., DUS:3R [5)] and MASt3R [5]],
provide an advanced tool for obtaining corresponding point maps, depth maps, and camera parameters from single or sparse views, facilitating the reconstruction of 3D point clouds. This approach offers a means to derive a coarse 3D structure and camera estimation from a single image.
3D Gaussian splatting. As a leading 3D representations, 3D Gaussian splatting (3DGS) (20] models the 3D scene by multiple 3D Gaussians parameterized by colors, centers, opacities, scales, and rotation quaternions. The effectiveness and efficiency of 3DGS in 3D reconstruction and generation have been widely demonstrated [20
27 29 In addition the L1 loss and SSIM loss optimizing a 3D scene's loss function typically B3 to improve optimization. The weights λς, ASSIM, and ALPIPS are adjustable hyperparameters. Formally, the specific loss function is expressed as:
45
44
26
LSSIM 663 presented in the original paper 20),
incorporates the LPIPS loss LLPIPS 66
L = X1 + ASSIMASSIM + ALPIPSLLPPPS.
3.2 Progressive scene expansion with persistent representation
To overcome the limitation of insufficient multi-views in single videos for continuous and flexible- view 3D scene generation (as discussed Sec. 2. S.1.2.IJ. we propose Flex World, which addresses this challenge by maintaining a persistent 3DGS representation. This persistent scene is initialized from an input image and incorporates information from multiple generated novel view videos through an iterative process. The process combines novel view synthesis, scene integration, and scene optimization, enabling progressive expansion of the viewable area while maintaining multi- view consistency. Fig. 2 provides an overview of the overall World framework. World is particularly effective for extreme viewpoint changes, such as full 360 scene generation. This section details the overall framework, while additional implementation specifics are provided in Appendix B
Novel view synthesis. To generate new 3D content or previously occluded objects in a persistent scene, our expansion process initiates with novel view synthesis on the existing scene. Our approach integrates existing scene information into the generation of new content to ensure geometric con- sistency. Specifically, we employ video-to-vides (V2V) diffusion model (see Sec. Sec.3..3) drawing inspiration from recent advances in [10, [1] which processes rendered video from the current scene as input and produces high-quality video from the same viewpoint as output. The V2V model enables flexible control over camera trajectories hidden within incomplete input videos to generate novel views. For example, when constructing 360 scenes, we first expand the initial view via zoom-out synthesis to establish the surrounding environment. The camera then alternates between 180 left and right rotations, progressively enriching details in each iteration, achieving 360 view scenes in 3 iterations.
Scene integration. New 3D content extracted from generated videos is then soometry-awareele incorporated into the persistent scene. To maintain global consistency, we propose an effective empirical approach for integrating 3D structures from sequential frames. We select m keyframes from the generated video facilitate the extraction of 3D content, i.e., point cloud. We utilize DUS(3R [50] to generate initial depth maps Do .... D, for each of the m keyframes Li,,., 4m and reference view lo simultaneously. For each view, render the corresponding incomplete depth maps Do...., D., from the existing scene, along with their masks My, Mnn The reference view is usually well optimized, and its rendered depth Do is completely known and can be used to measure the depth scale. For each 1 < < m, the new adding point cloud P. from view can be obtained by:
Median Do)), D., Dú, M.))
D; = Depth-align
Median (Do)
P. = (D.u,, u) " K- (u, 1))11|M;(u,, v) 1},
where E, denotes extrinsic for keyframe i, K denotes intrinsic, and (u, v) stands for the pixel coordinates of the frame, ranging from 0 to frame size. Median(-) represents extracting the median value from given depth map. By aligning the depth scale of the reference view, we mitigate the instability inherent in the depth estimation model. Depth-align(-) denotes any further depth alignment operation, implemented here via guided filtering [65] for smoother transitions. The resulting point clouds (Pi,..., ) are finally converted to 3DGS and merged into the persistent scene.
Scene optimization. The expanded scene further undergoes comprehensive optimization using all available video frames generated in the latest iteration. The scene is optimized using the loss function
5

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Ground Truth
MASSR S1
Ours
Frame
Frame 20
Frame 30
Frame
Figure 4: Our dataset construction method yields more accurate training pairs. We present frames of incomplete videos rendered from initial point clouds generated by a dense stereo model MAS(3 [5T] (i.e., ViewCrafter [I0]'s dataset construction method) and our 3DGS reconstruction. Our approach produces incomplete videos with better alignment to ground truth, resulting in higher- quality training pairs.
defined in Eq. (2). with hyperparameter details provided in Sec. 4 This refinement enhances visual quality and geometric accuracy while maintaining the unified structure of the persistent representation.
In contrast representative which employs transient point cloud representations that are re-estimated and discarded per iteration, approach maintains persistent 3D) structure throughout. This eliminates the need for repeated 3D)
generation methods based V2V models like ViewCrafter [10),
reconstruction from images, enabling both single-image scene generation and seamless expansion of existing scenes (Fig. D]. a capability unattainable by CiewCrafter. We provide discussion Sec. 4.4
and compare the performance of the two frameworks in generating 3D scenes in Appendix C.2
3.3 Improved diffusion for novel view synthesis
Although the framework presented Sec. 3.2 demonstrates feasibility, we aim for the V2V model to maintain content consistency across large camera variations. This capability would enable flexible- view scene generation with fewer iterations, thereby mitigating cumulative errors that could com- promise 3D scene coherence. However, existing V2V approaches [TO] [T] fail handle significant viewpoint changes (180"), as the results show in Fig. [ primarily due to using weaker base mod- els [57] (6 trained on suboptimal data, shown in Fig. We improve
V2V diffusion model by conducting video conditioning on an advanced base model and carefully designed training data.
Video conditioning. Our V2V diffusion architecture builds upon Cog VogeiooX-- [1], significantly more advanced foundation than previous baselines. We modify the original architecture by replacing image conditioning with video conditioning through 3D-VAE encoder. The encoded conditional videos are channel-wiss concatenated with noise latents during diffusion. Formally, given camera trajectory c, model learns the distribution incomplete video rendered from the coarse scene under c, and represents the target high-quality video. Notably, y may include unobserved black regions, and do not apply any special handling for these regions, allowing the use of the standard 12V model with minimal architectural changes. The training objective follows the standard diffusion formulation Eq.
p(zly), where represents the
Training data construction. Conventional training pairs generated by dense stereo models 10 50
51 often contain substantial geometric inaccuracies and texture artifacts (Fig. D. which impacted the generation quality of the trained V2V model (see Appendix A for further discussion). To overcome this limitation, we implement synthetic data generation pipeline with improved geometric fidelity:
Perform comprehensive 3DGS reconstruction using all available scene images; Select random frame, extract its depth map via 3DGS, and back back-project to obtain point cloud; Render incomplete video sequences y (49 consecutive frames) under continuous complex camera trajectories from datasets;

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Pair y with ground truth x to form training pairs (z,z).
This pipeline yields superior depth estimation accuracy, producing higher-quality initial point clouds, significantly improving training pair fidelity (Fig.
After training, our video model generalizes to generate high-quality novel views for coarse scenes from incomplete inputs under arbitrary trajectories, particularly under large camera variation (see Fig. and [0 Appendix E). This positions our model the optimal video diffusion model in Flex World, significantly enhancing the generation of flexible-view 3D scenes.
4 Experiment
We present the implementation details for FlexWorld comparison of novel view synthesis (video generation) and 3D scene generation, and show the ability of Flex World perform scene extrapolation sequentially. We also perform an ablation study Appendix C.2 and provide more results in Appendix
4.1
Implementation details
We build our video-to-video model based on the image-conditioned video diffusion model Cog -ideoX-5B-12V [5] The model is trained at a resolution of 480 720, with a learning rate of 5e-5 and a batch size of 32, for total of 5000 steps on 16 NVIDIA A800 80G GPUs. We retain the default settings for other hyperparameters in the original I2V fine-tuning process. In the training dataset, we utilize data from the DL3DV- 10K dataset (8), discarding any data with failed COLMAP camera annotations, which results a final set of 10253 3D scenes. The coefficients for the 3DGS loss function, specifically Åι ASSIM, and ALPIPS, set to 0.8, 0.2, and 0.3, respectively. More details can be found in Appendix B
4.2 Comparison on novel view synthesis
We evaluate the capability of our video-to-video model for novel view synthesis by comparing the visual generation quality and camera accuracy of open-source baseline models, including MotionCtrl ا CameraCtrl [9. DimensionX
B3, See3D [T], ViewCrafter [10]
Evaluation datasets. To ensure fairness, we selected the RealEstate (RE10K) test dataset [66 and Tanks-and- Teeppplls (Tanks) [17] datasets, which separate from our training dataset, for evaluation. Following previous work 12 10), we randomly selected 300 video clips with a sample stride ranging from to 3 in the RealEstaae OK In the manks-and-TTemlle dataset, randomly sampled 100 video clips with a stride of 4 across 14 test scenes, Notably, this dataset does not contain pre-labeled cameras; therefore, we utilized the MASt3R (51) model annotate the cameras. Each selected video clip involves a camera length of 49. For models generating fewer than 49 frames, we uniformly excluded cameras from the original trajectory to match the required length.
Evaluation metrics. We followed previous works [2 10 to evaluate the generated videos using various metrics comprehensively. The metrics include FID 677 and FVD [68] for assessing visual quality, as well as PSNR, SSIM frames and the ground truth, with the average of the calculated metrics for each frame taken. Additionally, we estimated the corresponding camera poses for each generated frame and the ground truth using MASt3R (51) for all models. The camera accuracy calculated using the formula from prior research 9
(3 and LPIPS (64] to evaluate the similarity between the generated
10 12
Qualitative comparison. From the qualitative comparison shown in Fig. all models exhibit certain level of control over camera movements, and methods like ViewCrafter, See3D, and Flex demonstrated relatively precise control; however, the visual quality of the generated outputs varied. The results from MotionCtrl often exhibited artifacts, while the content produced by CameraCtrl appeared somewhat blurred. See3I) struggled to generate distinct objects from novel viewpoints, and ViewCrafter produced dark content. In contrast, our method maintained effective camera control and surpassed all baseline models in the visual quality of the generated content.
For V2V- v2V-based models (i.e., See3D, ViewCrafter, Flex World.,,) we standardized the input by using MASI3R estimate both the initial point cloud and the camera poses, rather than using the original poses from the dataset.
7

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Input
Ground Truth
g
100 CamenaCrrl ( MotionCurl
View Crawerr
Sce3D
Ours
Figure 5: Qualitative comparison novel view synthesis. We assessed the generated videos from various models using the same camera trajectory, focusing on the midpoint. The green bounding box in the ground truth highlights regions requiring consistency with the input, while the remaining areas demand coherent content generation. The red bounding box marks low-quality outputs in baseline models. Our model demonstrates superior visual generation quality, even under effectively controlled camera conditions.
Quantitative comparisons. Our quantitative results are presented in Tab. | Flex World outperforms all baselines across datasets, achieving the best FID and FVD scores, indicating that generated content distribution closely aligns with the ground truth. It also attains optimal PSNR, SSIM. and LPIPS scores, demonstrating superior visual quality. Additionally, our model excels in camera control, with lower Rur and Tex values.
4.3 Comparison on scene generation
We mainly evaluate our method for 3D scene generation by comparing the visual quality of the render- ing results with 4 open-source baseline methods: LucidDreamer [4] DimensionX [13), See3D (II), and ViewCrafter [T]. Using the same sampling strategy as Sec. 4.2 we randomly selected 100 and 50 images from the RE 10K [16] and Tanks [17] datasets for evaluation. Except for LucidDreamer, which generates scenes using its original implementation, scenes for other methods are reconstructed from the videos generated to 3DGS, with reconstruction hyperparameters set in [3]. We choose PSNR, SSIM, and LPIPS for the evaluation metrics to compare the renderings from the generated 3D scenes by each baseline against the ground truth frames.
As illustrated in the qualitative comparison in Fig. 6 the scenes generated by Flex exhibit higher consistency with the content of the input images compared to other basclines. Furthermore,

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Table Quantitative comparison on novel view synthesis. Our method achieves superior visual quality while maintaining commendable camera control compared to the baselines.
Metric
FID 4
FVD +
PSNR 1
SSIM 1
LPIPS
Ren +
Ter +
RealEstate
MotionCtrl
20.41
226.62
13.19
0.516
0.515
0.141
0.216
CameraCtrl
22.73
381.38
16.03
0.604
0.416
0.040
0.117
DimensionX
33.77
548.19
11.77
0.491
0.659
0.864
0.615
See3D
24.24
259.62
14.44
0.546
0.477
0.026
0.355
ViewCrafter
16.99
143.89
15.74
0.595
0.372
0.032
0.380
World
13.88
100.41
16.62
0.612
0.344
0.026
0.297
Tanks and Temples
MotionCtrl
54.24
651.47
11.39
0.361
0.606
0.336
0.589
CameraCtrl
60.21
1338.53
11.08
0.363
0.688
0.202
0.535
DimensionX
54.13
1051.15
11.26
0.358
0.678
0.878
0.695
See3D
53.29
564.19
12.95
0.404
0.584
0.035
0.108
ViewCrafter
41.18
549.10
12.52
0.386
0.526
0.111
0.200
Flex World
37.31
376.49
13.20
0.405
0.525
0.048
0.100
Table Quantitative comparison on 3D scene generation. Scenes generated from single images by our method achieve nearly superior metric results across various datasets.
Dataset
Results on RealEstate IOK
Results on Tanks and Temples
Metric
PSNR 1
SSIM t
LPIPS
PSNR t
SSIM
LPIPS
LucidDreamer
13.03
0.498
0.590
11.67
0.342
0,661
DimensionX
11.55
0.438
0.718
11.02
0.308
0.700
See3D
14.60
0.544
0.483
12.82
0.396
0.584
ViewCrafter
15.06
0.562
0.446
12.35
0.356
0.581
FlexWorld 16.18
0.604
0.369
12.99
0.389
0.544
World generates content with higher visual quality in new regions beyond the input. We also conducted quantitative comparison, as presented in Tab. which shows that Flex World outperforms nearly all baselines terms of metries, with only slight decrease compared See3D on the SSIM in the Tanks [17] dataset. All results indicate that Flex World generates scenes with higher 3D consistency and visual quality.
4.4 Scene extrapolation
Leveraging the progressive expansion process, Flex can extend given 3D scene into a larger, more flexible-veww one, distinguishing it from methods like ViewCrafter [10). FlexWore can start with a 3DGS scene and iteratively expand it, as shown in Fig. [ with 3D scenes reconstructed from DL3DV [8), which Flex World further extrapolates into larger scenes. Our approach can also resolve artifacts such as holes or blurriness in the original, highlighting its scalability in generating high-quality scene extrapolation. See Appendix B.3 for detailed implementation and Fig. 13 in Appendix for more results.
4.5 Analysis flexible camera control
Flex World capable of generating novel view videos under flexible camera control. As quantified in Tab. [ it excels on challenging real-world datasets like RE10K [16] and Tanks [17]. This advantage particularly evident on the highly varied, complex, and non-linear camera paths characteristic of these benchmarks, Furthermore, we explicitly demonstrate its robustness to large camera motions.
As shown Fig. 3 and Appendix Fig. 10) our model maintains high-fidelity synthesis even under significant camera variations. Finally, the extensive result gallery in Appendix Fig. [2 provides additional qualitative evidence, highlighting the model's versatility across a wide array distinct camera trajectories.

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Input
Ground Truth
View ViewCrafer T0 ariidDramme i
Soc3D I
Ours
Figure 6: Qualitative comparison on 3D scene generation. We present images rendered from scenes generated by various single image-to-3D methods. The green and red bounding boxes have the same meaning as in Fig. 5 Our approach achieves superior visual results.
Conclusion
We propose Flex a framework for flexible-view 3D scene generation from single images using persistent 3D Gaussian representation. Our approach progressively expands this representation through novel view synthesis with fine-tuned V2V diffusion model, enabling robust handling of large viewpoint changes while maintaining visual consistency. Extensive experiments show Flex World's superior viewpoint flexibility and visual quality performance compared to baselines.
Limitations and broader impact. While FlexWorld shows promise for flexible 3D generation, limitations remain. The V2V model may lose camera control when the input lacks 3D information, though this can be alleviated by trajectory design. Additionally, Flex World's 3D consistency is affected by the dense stereo model's accuracy. Meanwhile, long generation time of the video diffusion model and the lengthy optimization process of iterative 3D Gaussian splatting constrain the efficiency of generating a single scene. Nevertheless, we believe that Flex World is promising and holds significant potential for VR and 3D tourism. As generative method, our method may be misused for data fabrication, necessitating strong safeguards against misuse.
Acknowledgements
This work was supported by the Beijing Natural Science Foundation (No. L247030); the National Natural Science Foundation of China (Nos. 924701 18, 62306163); the Beijing Nova Program (No. 202304844161 the ByteDance Research Fund; the Public Computing Cloud of Renmin University of China; the Beijing Major Science and Technology Project under Contract no. 7251 100008425002: and the fund for building world-class universities (disciplines) of Renmin University of China.
10

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A Impact of training data in video diffusion
Our data generation methodology is guided by the core principle of maintaining consistency between training and inference conditions. During inference, our model is designed to process incomplete views rendered from an optimized 3DGS scene with well-posed geometry. Therefore, directly using training pairs from conventional dense stereo models [10 50 [1] would introduce a significant domain gap. As shown in Fig. 4 these methods often produce training data with notable geometric inaccuracies and texture artifacts. More critically, these imperfections may be learned by the model and propagate into the final outputs, as evidenced by the artifacts in Fig. 7 which mirror those in the training data.
To further validate our approach, we conducted a brief experiment. We trained two models on the first IK samples of the DL3DV [88 dataset, one using training pairs generated by MAS13 (51) and the other by our methodology. Despite the limited training (1000 steps with a batch size of 8 on 8 A800 GPUs, compared to the full training detailed in Sec. B.I.. the visual results in Fig. clearly show that the model trained with our data generates qualitatively superior 360 3D scenes. This confirms that by aligning the training data with the inference conditions, our data generation methodology better harmonizes with the Flex World framework, ultimately enhancing the quality of the generated outputs.
B More implementation details
We present the detailed implementation details for Flex World in this section, including V2V model training. the complete Flexible-vies 3D scene generation workflow, the process of scene extrapolation, and the codebase and safeguards.
B.1 Training of V2V model.
The V2V model in Flex builds upon CogVidcoo-5B-I2V [15] and is fine-tuned using the SAT framework. Unlike the original 12V model, which processes a single image encoded by a 3D-VAE into latents with a temporal dimension of (later padded with zero tensors to match compressed video dimensions), our V2V model directly accepts video input. This eliminates the need for zero pero-padding, as the VAE naturally encodes the temporal dimension of the input video into compressed latents. As described in Sec. 4.1 we train the video-to- to-idoo model at a resolution of 480 x 720, with a learning rate of 5e-5 and a batch size of 32, for a total of 5000 steps on 16 NVIDIA A800 80G GPUs, requiring approximately 70 hours to complete.
As for the training datasets, to support the generation of static scenes and large camera variations, we select the high-quality DL3DV-10K [18) scene dataset, which contains various camera movements. After rigorous filtering process that excluded scenes with low image resolution (below 540 x 960), missing camera parameters, or significant pose discrepancies between the official DL3DV annotations and COLMAP reconstruction cache, we obtained 10253 high-quality 3D scenes for training. Each scene was reconstructed into 3D Gaussians using the official implementation, optimized for 7000 steps (taking about 5 minutes per scene completed in roughly 4-5 days by parallelizing across 8 A800 GPUs We exclude the RealEstate dataset [16] from our training dataset, its videos frequently contain moving objects and simple camera motions, which fail to meet our needs.
NVIDIA A800 GPU). The full dataset reconstruction was
B.2 Flexible-view 3D scene generation
Flex World begins with constructing initial 3D point cloud from a single input image using DUS(3R [50]. Since dense stereo requires paired images, we duplicate the single input image to serve as both the source and reference views.
As for scene representation, we employ 3DGS 20 as the core representation, utilizing gsplat [69 for implementation. This choice is motivated by two fundamental advantages over simpler point- cloud representations: rendering quality and functional capability. While raw point cloud serves as a geometric scaffold, rendering it directly often produces sparse results with holes and lacks photorealism. contrast, 3DGS models not only geometry but also local appearance, enabling the synthesis of high- fide-tiie anti-aliased novel views.
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Ours ViewCrafter 10
Figure 7: Artifacts generated by ViewCrafter [10]. Compared to World, ViewCrafter produces more artifacts that resemble those found in the incomplete videos within the training dataset con- structed by its method.
When processing point cloud data (e.g., the initial point cloud), it will be immediately converted into 3DGS, serving as the initial scene representation. Unlike the original 3DGS, which uses spherical harmonics, our implementation directly represents color using RGB values. We avoid downsampling during the initialization of 3DGS from the point cloud, so the number of Gaussian counts equals the number of point clouds. Gaussian properties are initialized directly from the point cloud's d' position and color, with scale and opacity set to isotropic values of 3e-4 and 0.8, respectively, and rotation initialized using the identity matrix.
For novel view synthesis, camera trajectories are interpolated between the first and last frames to produce 49 poses, matching the V2V model's input requirements. Spatial coordinates use linear and cubic spline interpolation, while rotation matrices employ spherical interpolation for smooth transitions. To avoid collisions, the movement range is constrained by the minimum depth derived from the input image's 's depth estimation.
When integrating new 3DGS into an existing scene, we utilize DUSt3R (50 to extract consistent depth from keyframes. We select m keyframes and employ the fully connected pairing strategy to achieve more accurate depth estimation. Keyframes are selected deterministically using a uniform sampling strategy, with the reference view typically chosen the input image's corresponding view due to its superior visual quality and role as the starting point for scene expansion. After depth alignment, utilize alpha maps masks rendered from the scene avoid the inclusion of redundant content. We apply 25 iterations of dilation to the alpha map to mitigate fragmentation in the added points.
During 3DGS optimization, we enhance visual quality by upscaling input video frames using the image super-resolution model Real-ESRGAN 170]. We use the original Gaussian paper's strategies for splitting, duplicating, and pruning Gaussians, but we disable the reset opacity strategy. Compared to the original Gaussian, we use higher learning rates: 1e-5 for position, 5e-3 for color, 5e-2 for opacity, 5e-4 for scale, and le-4 for rotation.
To further enhance the visual quality of the generated scene, adopt SDEdit 71 by rendering multi-view images from fixed viewpoints, adding random noise, and applying multi-step denoising process using the FLUUX.1-der 72) image diffusion model after the expansion of the scene. During the refinement process, the timestamp for the forward diffusion process is set at 0.6T, where T represents the total duration of the diffusion process. We focus refining images when rotating cameras rather than translating them. Specifically, we refine 5 frames from a panoramic scene using image-to-image refinement, followed by 1000 iterations of optimization with the same loss function in Eq. ( across all images to refine the overall Gaussian representation.
Flex World's full workflow is an iterative process that maintains persistent 3DGS scene as a coherent anchor. This ensures newly generated content aligns with the established structure, effectively reducing accumulated errors common in methods lacking such geometric memory. As described in Sec. 3.2. the complete Flex World pipeline requires three iterations generate a 360 scene. When exccuted on single NVIDIA A800 GPU, the entire process takes approximately 30 minutes.
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MAS13R 51
Ours
Figure 8: Comparison 360 scene generation results with different data generation method- ologies. The methodology proposed by FlexWorld yields more structurally consistent and coherent content compared dense stereo models, such as MAS13R [5T]
Table 3: Codebase. We provide the URL and licenses for the open-source assets we used.
Asset
URL
License
Models used in FlexWorld
15
https://github.
com/THUDD/CCogVidde
Apache-2.0 license
500
http:///ggtuub..com//vve/e/////d
CC BY-NC-SA 4.0 license CC BY-NC-SA license
httpe///gghhub..commaavrrr/ssss
69
Apache-2.0 license
Apache-2.0 license
70
BSD-3-Clause license
Baselines
http://ggaabbbcaa//leesttt///oo
Apache-2.0 license
9
Apache-2.0 license
13
Apache-2.0 license
Apache-2.0 license
Apache-2.0 license
CC BY-NC-SA 4,0 license
Datasets
18
https://gttuuu.comm/.00000/0///1
CC BY-NC 4.0 license CC BY 4,0 license
16
http::/www.taakkaaffteepeee....
CC BY 4.0 license
B.3
Scene extrapolation
Given an existing 3DGS scene, Flex World can be directly applied to expand into a larger, more flexible-view We directly the input 3DGS scene the initial persistent representation. rather than producing it with a single image. Then, through multiple iterative steps as described in progressively expand the scene, ultimately generating larger scene.
Section 3.2
To evaluate the capability of Flex World, we use the scene reconstructed from DL3DV as the input. The extrapolation uses two iterations with camera trajectories alternating between 180 left and right rotations, and the process takes approximately 20 minutes on single NVIDIA A800 GPU.
B.4 Codebase and safeguards
Table 3 lists the URLs and licenses of the open-source resources used this paper, including models used in Flex World, baselines for comparison, and training/evaluation datasets.
Notably, our work involves fine-tuning the publicly available Cog VideoX 15 model on
DL3DV [18] dataset, which contains almost no unsafe images. To ensure responsible usage, we will implement safeguards by enforcing strict controlled-use requirements when publicly releasing these resources.
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Table Quantitative comparison of novel view video generation using the WorldScore bench-
mark. For all metrics, higher scores indicate better performance.
WorldScore (Overall)
Camera Ctrl
Object Ctrl
Content Align
3D Consist
Photo Consist
Style Consist
Subjective Qual
Methods
ViewCrafter
66.81
78.25
48.92
50.63
81.88
74.90
68.05
65.01
See3D
62.86
85.21
55.40
54.46
86.22
85.05
44.74
28.91
FlexWorld
68.37
82.79
44.92
49.35
84.70
90.53
64.36
61.97
Table 5: Quantitative comparison of 3D scene generation using the WorldScore benchmark. Similar to the video evaluation, higher scores are better across all metrics.
WorldScore (Overall)
Camera Ctrl
Object Ctrl
Content Align
3D
Photo Consist
Style Consist
Subjective
Methods
Consist
Qual
ViewCrafter
66.20
81.02
69.00
47.77
83.46
81.30
64.88
35.94
Sce3D
59.48
81.22
57.42
44.94
82.97
83.15
46.38
20.30
FlexWorld
67.65
80.35
69.50
46.27
87.47
88.42
61.72
39.84
C Additional experimental results
In this section, we present additional experiments assess our model's performance further, followed by detailed ablation study to validate our design choices.
C.1 Evaluation with WorldScore
To provide a more comprehensive assessment of generation quality, we conduct further evaluations using 3D-centric metric WorldScore benchmark [73), with the two main basclines, See3D [[]] and ViewCrafter [10]. For the video generation task, we test on diverse subset of 300 scenes, created by selecting the first 15 scenes from each of the 20 available categories. This ensures representative sample covering various conditions (e.g., indoor/outdoor, stylizee/photoreaalitie))) For the more computationally intensive 3D scene generation task, evaluate the first 100 scenes from this subset. All evaluations strictly adhere to the official WorldScore protocol, and the generation methods for all models are consistent with those described in Sec. 4.2 and 4.3
The results are presented Tab. and Our model achieves a superior overall WorldScore both video and 3D scene generation, underscoring its robust performance. It noteworthy that our method demonstrates significant advantages even on the WorldScore benchmark, which features relatively simple camera trajectories. This highlights the general effectiveness and versatility of our approach beyond complex camera paths.
C.2 A blation study
We conduct comprehensive ablation studies validate the effectiveness of each key component in Flex World. We provide both qualitative results in Fig. Fig. a quantitative analysis in Tab. 6) For the quantitative study, again utilize the WorldScore benchmark (73) to evaluate videos rendered from the generated 360" scenes. Due to computational constraints, this analysis was performed on a subset of 155 cases (the first 31 images from the first categories). We evaluate five main configurations to isolate the contribution of each component.
Ablation on video diffusion. As shown in Fig. 9h, replacing our V2V model in Flex World with ViewCrafter resulted in blurred scene content. This is due to inconsistencies ViewCrafter's output under large camera variations, discussed in Sec. 3.3 The quantitative results in Tab. (row vs. row 4) corroborate this finding, showing a substantial drop in performance across all metrics.
Ablation camera trajectory. A zoom-out movement is crucial for enlarging the scene to enhance camera control. Without the generated video will mismatch with the input trajectory, leading to inconsistencies and blurriness in the generated scene, shown in Fig, 9p.
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Table 6: Quantitative ablation study using the WorldScore benchmark. We analyze the impact of different components and framework configurations. Higher is better for all metrics, and the Camera complex than the
Control metric is not applicable our final scene camera movements WorldScore defaults.
Configuration
WorldSeore (Overall)
Object Ctrl
Content Align
3D Cousist
Photo Cousist
Style Consist
Subjective Qual
Viewcrafter's V2V frumework (w/o refine)
44.12
65.81
39.93
61.40
0.00
89,40
52.33
Our V2V+ ViewCrafter style's 's framework (w/o refine)
49.97
70.16
55.66
62.66
37.77
86.60
36.95
framework (wlo super-resolution who seline)
56.15
57.25
51.72
96.30
Our framework (wlo refine)
56.34
73.06
58.30
71.07
46.62
96.48
48.84
Our full framework (w/ refine)
55.60
71.29
57.09
70.81
45.69
96.66
47.67
(a) w/o V2V
(b) w/o zoom-out (c) w/o framework
(d) w/o refine
(e) Full
Figure 9: Ablation study. To generate a 360 view scene, Flex World necessitates our video-to- video model, an initial zoom-out trajectory and generation framework. Additionally, a refinement process can further enhance the visual quality of the generated 3D scene.
Ablation on generation framework. As the scene generation framework of ViewCrafter [0] remains unopen, reimplement its framework following its described pipeline and generate 3D scenes our V2V model for fair comparison. When using our V2V model with Viewcrafter's framework, the resulting scene quality is visibly lower (Fig. (9)) and scores poorly in the quantitative evaluation (Tab. [6] row 2 vs. row 4). In contrast, our proposed framework not only produces higher-quality results but also uniquely supports scene extrapolation, as detailed in Section 3.2
Ablation on super-resolution. We optionally incorporate Real-ESRGAN super-resolution module to enhance the texture detail of the generated videos. However, ablation in Tab. (comparing rows 3 and 4) reveals that this step has a negligible impact on the WorldScore metrics. This suggests that while it may improve final visual appeal, it is not essential for constructing the core 3D geometry and structure.
Ablation on refinement process. The video model's generation quality restricts the detail in the generated scene. refinement process, as detailed in Appendix B further modestly enhances the generated visual details while preserving the existing geometric structure of the scene, shown in Fig. Dd. And the process slightly influences metries WorldScore (comparing rows 4 and 5 in Tab.
D Limitations and future work
limitation of our current framework is its computation time of approximately 30 minutes per scene (see Appendix B.2 Notably, this is comparable with previous work (c.g.. ViewCrafter) under similar conditions. Our observations indicate that the primary bottlenecks are the video diffusion model's generation time and the iterative 3D Gaussian splatting optimization. Furthermore, we recognize that inaccuracies in the initial depth estimation may introduce accumulated errors during the reconstruction process. Crucially, these limitations can be substantially mitigated by leveraging rapid advancements in foundational models. We are confident that this limitation will be alleviated by leveraging advances in base models, such as distilled video generators 74 for accelerated sampling and feed-forward reconstruction 3DGS models (75 to bypass iterative optimization. Similarly, the impact accumulated errors could be reduced by employing improved depth estimators (e.g.. VGGT (76)). Integrating these is promising direction for future work.
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View Craller 10
Sec3D 1
Ours
View 15.0
View 52.5*
View 165.0'
View 172.5
ViewCrafter 10
Soc3D i
Ours
View 30.0
View 37.5
View 150.0
View 157.5
Figure 10: More comparative results showcasing generated videos under large camera variations.
E
More results
We present more results of comparison generated videos under large camera variations in Fig. 10
our video generation Fig. 12 the 360 3D scene generation in Fig. and the 3D scene extrapola- tion in Fig. 13
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Vitwo
120
View
View 2409
View
1500
300
ED
3T1
Input
Flexible-view 3D scene generated by FlexWorld
Figure 11: More results of generated 360 scene from Flex World.
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Input
Videos generated by our V2V model given camera trajectorics
Figure 12: More results of generated videos from Flex World.
29

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Input
Output
Input
Output
Inpar
Output
Input
Output
Figure 13: Scene extrapolation. We show Flex World's ability extend existing scenes beyond their original boundaries. The results presented in 360 panoramas, where the top image in each line illustrates the incomplete original scene, and the bottom image reveals the extrapolated one generated by World.
30


Reviews:
[{"review_id": "4aF44kKk0y", "reviewer": "Reviewer_wGjX", "summary": "The 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Most of my concerns have been addressed. I appreciate the efforts in preparing the dataset. It would be great if the authors have the plan to release them for public access.\n\nI do not further raise my rating because of the novelty concern.", "rebuttal": "We would like to express our sincere gratitude for the valuable time you have dedicated to the peer review process and for your thorough examination of our work. Your constructive comments have been instrumental in enhancing the quality of our manuscript. We have carefully considered all suggestions and will incorporate the corresponding revisions in the final version of our manuscript. Below is our point-by-point response to your comments.\n\n#### **Weakness 1: Incremental Novelty**\n\nWe sincerely thank you for your insightful feedback and for acknowledging the effort involved in our data preparation. We appreciate this opportunity to clarify our core novelty, which lies not only in combining existing components but more importantly, in the architectural design of our scene generation framework and the unique capabilities it enables.\n\nFirst, our primary contribution is a novel framework centered around a persistent 3D structure. This allows us to **leverage rich geometric and color information from the existing scene** to guide subsequent expansions into a final, flexible-view 3D scene. This stateful, 3D-aware approach is a fundamental departure from frameworks like ViewCrafter, which **discard intermediate geometry after each step** and instead construct temporary 3D structures using only historical RGB information. Our framework leads to superior final results, as demonstrated in Fig. 8 by the comparison between setups \\(c\\) and (d).\n\nWe have conducted a new quantitative experiment to further validate the effectiveness of our main contribution, as suggested by reviewers v55n and GLFg. We **carefully isolated the framework's contribution** by comparing two setups using the same V2V model and no image refinement: (1) a ViewCrafter-style framework and (2) our proposed framework. We generated 360° 3D scenes for 155 test cases (due to time constraints, we selected the first 5 out of 20 total categories and the first 31 out of 100 images from each, totaling 155 scenes for evaluation) under five key configurations, rendered 360° videos from these scenes, and then evaluated the videos using the official WorldScore protocol. The results below demonstrate that our framework architecture consistently outperforms the alternative across all relevant metrics.\n\n\n| Configuration                                            | WorldScore (Overall) | Camera Ctrl | Object Ctrl | Content Align | 3D Consist | Photo Consist | Style Consist | Subjective Qual |\n| -------------------------------------------------------- | -------------------- | ----------- | ----------- | ------------- | ---------- | ------------- | ------------- | ------------------ |\n| Our V2V+ ViewCrafter style's  framework (w/o refinement) | 49.97                | N/A         | 70.16       | 55.66         | 62.66      | 37.77         | 86.6          | 36.95              |\n| Our framework (w/o refinement)                           | 56.34                | N/A         | 73.06       | 58.3          | 71.07      | 46.62         | 96.48         | 48.84              |\n\n\n(Note: Higher is better for all metrics. The Camera Control metric is not applicable (N/A) as our final scene camera movements are more complex than the WorldScore defaults.)\n\nFurthermore, this persistent 3D structure uniquely endows our framework with the ability to perform **scene extrapolation**, as detailed in Sec. 4.4, allowing our model to seamlessly extend an existing 3D scene, **a capability not present in prior works like ViewCrafter or See3D**. We believe these supplementary explanations demonstrate that our framework design is, in itself, a novel and impactful contribution. We will add this quantitative study to the ablation section in our revised manuscript.\n\n#### **Weakness 2: Computational Cost**\n\nWe thank the reviewer for this important point. While the 30-minute computation time per scene is a current limitation, our method's runtime for generating a 3D scene is comparable to that of ViewCrafter under the same settings. \n\nMoreover, this issue will be addressed in future work without altering the core components of our approach. Our analysis indicates the primary bottlenecks are the video diffusion model's generation time and the iterative 3D Gaussian Splatting optimization. However, this can be substantially mitigated by leveraging rapid advancements in foundational models. For instance, recent progress in video model distillation [1] is dramatically reducing inference time, while emerging feed-forward 3DGS methods [2] can replace the costly optimization step. These technologies can be integrated into our framework in the future to make it significantly more efficient. To ensure full transparency, we will add a detailed discussion of this limitation and these potential future improvements to the \"Limitations\" section of our revised manuscript.\n\n[1] Yin T, Zhang Q, Zhang R, et al. From slow bidirectional to fast causal video generators[J]. arXiv e-prints, 2024: arXiv: 2412.07772.\n\n[2]Jiang L, Mao Y, Xu L, et al. AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views[J]. arXiv preprint arXiv:2505.23716, 2025.\n\n#### **Question 1: \"3D Scene Generation\" vs. \"Novel View Synthesis\"**\n\nWe thank the reviewer for highlighting this lack of clarity and apologize for the confusion. We will explicitly define these terms in our final manuscript. In our work, we use them to distinguish two sequential stages:\n\n1.  **Novel View Synthesis (NVS):** This refers specifically to the output of the video diffusion model, which generates a video clip simulating camera movement through a scene. It is fundamentally a 2D video generation task, creating a sequence of new views.\n2.  **3D Scene Generation:** This refers to the subsequent process where we use the generated video to construct an explicit and renderable 3D scene representation (i.e., 3D Gaussian Splatting). This final 3D asset can be rendered from any arbitrary viewpoint.\n\nThis distinction is adopted from the influential ViewCrafter paper [3], where our \"Novel View Synthesis\" is analogous to their video generation step (Fig. 3), and our \"3D Scene Generation\" corresponds to their \"scene reconstruction\" stage (Fig. 4). We will add these clarifications to the introduction of our revised manuscript.\n\n[3] Yu W, Xing J, Yuan L, et al. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis[J]. arXiv preprint arXiv:2409.02048, 2024.\n\n#### **Question 2: Number of Training Samples**\n\nThank you for this question. We trained our model on **10,253** successfully reconstructed 3D scenes from the DL3DV dataset, a widely-used collection of 3D data. This number was reached after a rigorous filtering process where we first excluded scenes with low image resolution (< 540x960) or missing camera parameters. Subsequently, we cross-validated the camera poses between the official DL3DV annotations and a COLMAP reconstruction cache, discarding any scenes with significant discrepancies to ensure high-quality data. We will update the manuscript to include these specific details about our dataset preparation. To further enhance reproducibility, we also commit to making our data processing scripts publicly available upon publication.\n\n\n#### **Question 3: Computational Cost for Fitting Gaussians**\n\nThank you for your question regarding the reconstruction cost. For each of the 10,253 scenes in our training set, we fit the 3D Gaussians using the official implementation, running the optimization for 7,000 steps. This process takes approximately **5 minutes per scene** on a single NVIDIA A800 GPU. By parallelizing the workload on a server equipped with 8 NVIDIA A800 GPUs, we were able to complete the full dataset reconstruction in approximately 4-5 days. We will add these computational specifics to the implementation details section of our manuscript for transparency and reproducibility.\n\n\nThank you again for your valuable comments and suggestions. We believe that the clarifications and supplementary experiments, made in response to your feedback, have appropriately addressed your concerns and further strengthened the persuasiveness of our paper."}, {"review_id": "8N4QB9l6Dw", "reviewer": "Reviewer_v55n", "summary": "FlexWorld 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.", "questions": "- 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?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "Authors addressed my concerns regarding using complicated super resolution and refinement (flux) in their pipeline. The ablation shows that the core of the method, V2V on new dataset, yields the most significant contribution. I agree with reviewers that the V2V is not novel and dataset pipeline is the main novelty. The evaluations are thorough and the dataset pipeline is also well documented.", "rebuttal": "We sincerely appreciate the time and effort you have devoted to reviewing our manuscript and providing insightful feedback. Your thoughtful comments have significantly contributed to improving the quality of our work. We have thoroughly addressed each of your suggestions and will implement the necessary revisions in the final version of the manuscript. Below, we provide detailed responses to each of your points.\n\n#### **Weakness 1: Limited Technical Novelty**\n\nWe thank you for your feedback. While we agree that our method leverages a strong base model and an effective data construction strategy, we would like to respectfully clarify that our core technical novelty lies in the architectural design of our generation framework itself.\n\nOur primary contribution is a novel framework that maintains and iteratively refines a **persistent 3D structure**. (as detailed in Lines 146-148). This allows us to leverage rich geometric and color information from the existing scene to guide subsequent expansions into a final, flexible-view 3D scene. This stateful, 3D-aware approach is a fundamental departure from frameworks like ViewCrafter, which discard intermediate geometry after each step and instead construct temporary 3D structures using only historical RGB information. This framework modification leads to superior final results, as demonstrated in Fig. 8 by the comparison between setups \\(c\\) and (d), and is also supported by the table in the ablation study conducted at your suggestion (presented in the response to Weaknesses 2 & 3 & Question 1). Furthermore, this persistent structure enables **a unique capability not present in previous methods: 3D scene extrapolation, allowing our model to seamlessly extend a scene beyond its initial boundaries** (Sec. 4.4).\n\nWe believe these supplementary explanations demonstrate that our framework design is, in itself, a novel and impactful contribution. We will add this new quantitative study and its corresponding analysis to our revised manuscript.\n\n\n#### **Weaknesses 2 & 3 & Question 1: Pipeline Complexity and Lack of Quantitative Ablation**\n\nWe sincerely thank the reviewer for this insightful and constructive feedback. We agree that a quantitative ablation study is crucial for clarifying the contributions of each component in our framework and for providing a clearer comparison with strong baselines (i.e., ViewCrafter).\n\nFirst, for the sake of clarity, we wish to address the concern about the refinement module's impact on our main quantitative comparisons. As stated in the original manuscript (Lines 229-231), the results in Table 2 follow the experimental protocol of ViewCrafter, where metrics are calculated from 3D scenes reconstructed from videos generated *without* FLUX refinement. Therefore, the use of FLUX for refining the 360° flexible-view 3D scene shown in Fig. 1b does not influence those specific results.\n\nNevertheless, we fully agree that a quantitative ablation study is crucial for delineating the contribution of each component. Following your suggestion, we have conducted a new quantitative ablation study. Measuring the quality of 360° scenes is challenging; fortunately, a recently released **WorldScore** benchmark [1] (April 2025, also suggested by Reviewer GLFg) provides a way for this. For this study, we generated 360° 3D scenes for 155 test cases (due to time constraints, we selected the first 5 out of 20 total categories and the first 31 out of 100 images from each, totaling 155 scenes for evaluation) under five key configurations, rendered 360° videos from these scenes, and then evaluated the videos using the official WorldScore protocol. The five configurations are:\n1.  ViewCrafter's V2V Model + Our Framework (with super-resolution, without refinement)\n2.  Our V2V Model + ViewCrafter's Framework (with super-resolution, without refinement)\n3.  Our Framework (without super-resolution or refinement)\n4.  Our Framework (with super-resolution, but without refinement)\n5.  Our Full Framework (with super-resolution and refinement)\n\nThe results are presented below. (Note: Higher is better for all metrics. The Camera Control metric is not applicable (N/A) as our scenes involve complex camera paths.)\n\n| Configuration                                                | WorldScore (Overall) | Camera Ctrl | Object Ctrl | Content Align | 3D Consist | Photo Consist | Style Consist | Subjective Qual |\n| ------------------------------------------------------------ | -------------------- | ----------- | ----------- | ------------- | ---------- | ------------- | ------------- | ------------------ |\n| (1) Viewcrafter's V2V+ our framework (w/o refinement)        | 44.12                | N/A         | 65.81       | 39.93         | 61.4       | 0.0           | 89.4          | 52.33              |\n| (2) Our V2V+ ViewCrafter style's  framework (w/o refinement) | 49.97                | N/A         | 70.16       | 55.66         | 62.66      | 37.77         | 86.6          | 36.95              |\n| (3) Our framework (w/o super-resolution, w/o refinement)     | 56.55                | N/A         | 72.74       | 57.25         | 69.85      | 51.72         | 96.3          | 47.96              |\n| (4) Our framework (w/o refinement)                           | 56.34                | N/A         | 73.06       | 58.3          | 71.07      | 46.62         | 96.48         | 48.84              |\n| (5) Our full framework (w/ refinement)                       | 55.6                 | N/A         | 71.29       | 57.09         | 70.81      | 45.69         | 96.66         | 47.67              |\n\n\nThis comprehensive ablation provides several key insights that quantitatively support some of the qualitative results in Figure 8:\n\n* **Impact of our V2V Model**: Comparing **(1)** and **(4)** demonstrates the superiority of our trained V2V model. It significantly boosts performance across nearly all metrics, which aligns with the qualitative improvements shown between Fig. 8a and Fig. 8d.\n* **Impact of our Framework**: Comparing **(2)** and **(4)** highlights the effectiveness of our core framework design. When both use our V2V model, our framework substantially outperforms the ViewCrafter-style framework, confirming the observations from Fig. 8c and Fig. 8d and supporting the claim in the responses to Weakness 1.\n* **Impact of Super-Resolution**: The difference between **(3)** and **(4)** is minimal, indicating that the Real-ESRGAN super-resolution component is an optional step for potentially enhancing visual detail and not a primary contributor to our framework.\n* **Impact of FLUX Refinement**: Finally, comparing **(4)** and **(5)** directly addresses your question about the refinement step. As we elaborate in the Appendix (Line 492), the refinement step is used solely to enhance the visual quality of the generated scenes (which is consistent with the qualitative results in Fig. 8d and 8e), and it even leads to a slight decrease in comprehensive 3D metrics like WorldScore.\n\nWe believe this new study thoroughly clarifies the contribution of each component and quantitatively validates the ablations presented in Figure 8. We will incorporate a full quantitative ablation study and our analysis into the \"Ablation study\" subsection in the revised manuscript.\n\n[1] Duan H, Yu H X, Chen S, et al. Worldscore: A unified evaluation benchmark for world generation[J]. arXiv preprint arXiv:2504.00983, 2025.\n\n\n#### **Question 2: Handling of Unobserved Regions**\n\nThank you for this insightful question regarding our training data. In our current implementation, we do not apply any special handling (e.g., masking or inpainting) for the black, unobserved regions in the rendered training videos. This design choice was made to simplify the data framework and use a standard I2V model for our V2V task with minimal architectural changes. The model learns to interpret these unobserved areas as part of the input condition alongside the visible scene content. We will add sentences to the implementation details section of our manuscript to clarify this point.\n\n#### **Question 3: Dataset Filtering and Camera Trajectories**\n\nThank you for asking for these important details.\n\n**Dataset Filtering:** We performed a rigorous, multi-step filtering process on the DL3DV-10K dataset. First, we discarded scenes with low image resolutions (below 540x960) or incomplete camera annotations. Next, as a critical quality control step, we verified consistency between the provided DL3DV camera parameters and those from a COLMAP reconstruction, removing any samples with significant discrepancies. This yielded our final training set of 10,253 high-quality scenes.\n\n**Camera Trajectories:** The trajectory choice was straightforward. As described in the manuscript, for each scene, we simply sample one continuous camera trajectory of a fixed length (49 frames) at random from the scene's original sequence.\n\nWe will add these explicit details to the revised manuscript. To fully support reproducibility, we also affirm our commitment to releasing the data construction code upon the paper's publication.\n\nWe are truly grateful for your time and expertise in reviewing our work. Your comments were invaluable in guiding our revisions, and we hope that the additional clarifications and experiments now fully address your concerns while improving the overall quality of the paper."}, {"review_id": "fwKyD5ZbFX", "reviewer": "Reviewer_GLFg", "summary": "FlexWorld 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.", "questions": "Please refer to Weaknesses.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "After the rebuttal, most of my concerns are solved, so I raise my final rating.", "rebuttal": "We sincerely thank you for your time and careful review of our manuscript. Your insightful feedback has been invaluable in enhancing our work. We will carefully incorporate all of your suggestions in the revised manuscript. Our point-by-point responses to your comments follow below.\n\n#### **Weakness 1: Novelty of the Pipeline**\n\nWe thank you for your feedback. We would like to respectfully clarify how our framework's architecture represents a significant and novel departure from established approaches.\n\nThe core distinction lies in our use of a **persistent 3D scene**, as detailed in Sec 3.2. This allows us to leverage rich geometric and color information from the existing scene to guide subsequent expansions into a final, flexible-view 3D scene. This stateful, 3D-aware approach is a fundamental departure from frameworks like ViewCrafter, which **discard intermediate geometry after each step** and instead construct temporary 3D structures using only historical RGB information. This framework modification leads to superior final results, as demonstrated in Fig. 8 by the comparison between setups \\(c\\) and (d).\n\nWe have conducted a new quantitative experiment inspired by your and reviewer v55n's suggestions to further validate the superiority of our framework design. We **carefully isolated the framework's contribution** by comparing two setups using the same V2V model and no image refinement: (1) a ViewCrafter-style framework and (2) our proposed framework. We generated 360° 3D scenes for 155 test cases (due to time constraints, we selected the first 5 out of 20 total categories and the first 31 out of 100 images from each, totaling 155 scenes for evaluation) under five key configurations, rendered 360° videos from these scenes, and then evaluated the videos using the official WorldScore protocol. The results below demonstrate that our framework architecture consistently outperforms the alternative across all relevant metrics.\n\n| Configuration                                            | WorldScore (Overall) | Camera Ctrl | Object Ctrl | Content Align | 3D Consist | Photo Consist | Style Consist | Subjective Qual |\n| -------------------------------------------------------- | -------------------- | ----------- | ----------- | ------------- | ---------- | ------------- | ------------- | ------------------ |\n| Our V2V+ ViewCrafter style's  framework (w/o refinement) | 49.97                | N/A         | 70.16       | 55.66         | 62.66      | 37.77         | 86.6          | 36.95              |\n| Our framework (w/o refinement)                           | 56.34                | N/A         | 73.06       | 58.3          | 71.07      | 46.62         | 96.48         | 48.84              |\n\n(Note: Higher is better for all metrics. The Camera Control metric is not applicable (N/A) as our final scene camera movements are more complex than the WorldScore defaults.)\n\nFurthermore, this persistent 3D structure uniquely endows our framework with the ability to perform **scene extrapolation**, as detailed in Sec. 4.4, allowing our model to seamlessly extend an existing 3D scene, **a capability not present in prior works like ViewCrafter or See3D**. We believe these supplementary explanations demonstrate that our framework design is, in itself, a novel and impactful contribution. We will add this quantitative study to the ablation section in our revised manuscript.\n\n#### **Weakness 2: Justification for Using 3DGS**\nThank you for this important question. Our choice of 3D Gaussian Splatting (3DGS) over simpler point-cloud representations from methods like DUSt3R is motivated by two fundamental advantages essential to our framework: rendering quality and functional capability.\n\nFirst, regarding quality, a raw point cloud is merely a geometric scaffold. Rendering it directly often produces sparse results with holes and lacks photorealism. In contrast, 3DGS is a complete scene representation where each Gaussian splat models not just geometry but also local appearance, enabling the synthesis of high-fidelity, anti-aliased novel views that capture complex view-dependent effects. Furthermore, our method operates with known camera trajectories, making direct reconstruction into a high-fidelity 3DGS representation a more natural and effective choice compared to methods using unknown camera trajectories like VGGT and DUSt3R.\n\nMost critically, however, 3DGS is inseparable from our **progressive scene expansion** process. A 3DGS scene is composed of explicit, editable primitives. This property is foundational to our method, as it allows our framework to iteratively fuse new information and seamlessly expand the 3D scene. This dynamic, progressive construction is a core novelty of our work and is fundamentally different from the static, holistic point-cloud outputs generated by methods like DUSt3R.\n\nIn summary, 3DGS is not merely a post-processing choice for better visuals; it is a foundational component that enables the core functionality of our method. We are very grateful for your suggestion which prompted this discussion, and we will add this explanation to the revised manuscript.\n\n\n#### **Weakness 3: Evaluation Metrics**\n\nWe sincerely thank you for this excellent and highly constructive suggestion. Adopting a 3D-centric metric is indeed crucial for a comprehensive assessment. Following your advice, we have conducted a new evaluation using the official **WorldScore benchmark**.\n\nDue to the time constraint, we performed a head-to-head comparison between our method and the baseline, ViewCrafter, and evaluated a substantial subset of 300 scenes from the WorldScore static test dataset (2000 total). The subset was created by systematically selecting the first 15 scenes from each of the 20 categories to ensure diversity (including indoor/outdoor, stylized/photorealistic scenes). The evaluation followed the official WorldScore protocol, with the generation methods for both the test videos and 3D scenes remaining consistent with Sections 4.2 and 4.3 in the original paper, respectively. Due to the time limitation, we chose the first 100 scenes to do a 3D scene generation evaluation. The detailed results are presented in the table below (note: higher is better for all metrics):\n\nQuantitative comparison of **camera‐controlled video generation**:\n| Methods     | WorldScore (Overall) | Camera Ctrl | Object Ctrl | Content Align | 3D Consist | Photo Consist | Style Consist | Subjective Qual |\n| ----------- | ----------------- | ----------- | ----------- | ------------- | ---------- | ------------- | ------------- | ------------------ |\n| ViewCrafter | 66.81             | 78.25       | 48.92       | 50.63         | 81.88      | 74.9          | 68.05         | 65.01              |\n| Ours        | **68.37**             | 82.79       | 44.92       | 49.35         | 84.7       | 90.53         | 64.36         | 61.97              |\n\nQuantitative comparison of **underlying scene quality**:\n| Methods      | WorldScore (Overall) | Camera Ctrl | Object Ctrl | Content Align | 3D Consist | Photo Consist | Style Consist | Subjective Qual |\n| :---------- | :------------------: | :---------: | :---------: | :-----------: | :--------: | :-----------: | :-----------: | :--------------: |\n| ViewCrafter |         66.2         |    81.02    |    69.0     |     47.77     |   83.46    |     81.30     |     64.88     |      35.94       |\n| Ours        |      **67.65**       |    80.35    |    69.5     |     46.27     |   87.47    |     88.42     |     61.72     |      39.84       |\n\n\nThe results from the WorldScore evaluation show that our model achieves better overall scores, which confirms its strong performance and aligns with the findings in Tables 1 and 2 of our original manuscript. Notably, our method still shows improvement even though the camera movement in the WorldScore benchmark is relatively simple. Moreover, our approach demonstrates a more significant advantage in scenarios with large camera variations, as illustrated in Figure 3 and 9 of the manuscript.\n\nIn conclusion, this new, more comprehensive 3D-centric evaluation confirms the superiority of our method over the baseline. We will conduct these new experiments on the complete benchmark and add the results to the experimental section of our revised manuscript.\n\n#### **Weakness 4: Accumulated Error**\n\nThank you for this critical question regarding error accumulation during progressive scene expansion. While it is common for accumulated errors to arise in multi-round iterative generation frameworks, our framework is specifically designed to mitigate this issue.\n\nAs mentioned in our response to Weakness 1, the core of our approach is the maintenance of a **persistent 3D structure**. At each expansion step, the existing 3D scene (including its geometry and color) serves as a strong, coherent anchor for the generation of the next video segment. This ensures that newly generated content is well-aligned with the established scene, fundamentally reducing the kind of accumulated error that can occur in frameworks that lack this persistent geometric memory.\n\nBy the way, we recognize that depth estimation inaccuracies may introduce accumulated errors, though improved estimators (e.g., VGGT) could reduce them. This limitation will be further discussed in the final manuscript.\n\n\nThank you again for your constructive comments. We believe the revisions and additional experiments, undertaken in response to your feedback, have thoroughly addressed your concerns and will strengthen the overall quality of our manuscript."}, {"review_id": "n1FU0l7lN0", "reviewer": "Reviewer_gXbR", "summary": "This 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.", "questions": "* 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "My main concerns were that some of the key claims, such as support for flexible camera control, were not well-supported by experiments, and the dataset generation process was not clearly explained or sufficiently discussed.The authors have agreed to polish the paper further, emphasizing the data generation pipeline and including experiments to better support the claim of flexible camera support. Therefore, I am updating my rating from borderline reject to borderline accept. My remaining concern is that the method relies heavily on the 3DGS representation and a V2V model, both of which are based on prior work. In my view, the main novel contributions lie in the proposed data generation process and the way generated images are merged into the 3DGS representation, both of which may have limited novelty.", "rebuttal": "We sincerely thank you for the thorough, insightful, and highly constructive review. Your detailed feedback has been incredibly helpful in strengthening our manuscript. We have carefully considered all your comments and questions, and we provide our point-by-point responses below.\n\n#### **Weakness 1: Writing Clarity**\n\nWe sincerely thank you for your valuable and detailed feedback on the manuscript's writing and structure. We agree that clarity and conciseness are paramount for conveying our work effectively. Based on your constructive suggestions, we will undertake a thorough revision of the manuscript.\n\nSpecifically, we will perform a full review to shorten overly long sentences and improve overall readability. The specific sentences you highlighted will be revised. For example, the sentence on Lines 117–119 will be revised from \"Unlike most methods that generate solely from given images..., our approach integrates current scene information into newly generated content.\" to a more direct statement, such as: **\"Our approach integrates existing scene information into the generation of new content to ensure geometric consistency.\"** Similarly, the sentence on Lines 127–129 will be revised from \"While deriving 3D structures from sequential frames and maintaining consistency... presents significant challenges..., we propose an effective empirical approach.\" to focus on our solution, for example: **\"To maintain global consistency, we propose an effective empirical approach for integrating 3D structures from sequential frames.\"**\n\nFurthermore, as you correctly pointed out, we will explicitly reference **Figure 2** at the beginning of Section 3.2 to provide readers with a clear, high-level overview of our framework from the outset. We are grateful for these actionable comments and are confident these revisions will substantially improve the clarity of our paper.\n\n#### **Weakness 2: Support for Flexible Camera Control**\n\nWe thank you for this valid and constructive criticism. We agree that our initial manuscript claimed \"flexible camera control\" without providing a dedicated analysis to substantiate it. Your feedback has helped us significantly improve the validation of this key strength of our method.\n\nTo rectify this, we will add a new subsection to the paper (e.g., Section 4.5) titled \"**Analysis of Flexible Camera Control**.\" In this new section, we will consolidate and reframe existing evidence from our paper to explicitly support our claim:\n\n* **Performance on Diverse and Complex Trajectories**: We first argue that our model's superior performance on challenging real-world datasets like RealEstate10K and Tanks and Temples is strong evidence of its flexibility. These datasets usually feature highly varied, complex, and non-linear camera paths. Our quantitative results on these datasets, presented in Table 1, demonstrate that our model robustly handles these diverse trajectories compared to other baselines.\n* **Robustness to Large Camera Variation**: We now explicitly draw the reader's attention to figures that specifically test the limits of camera motion. We highlight that Figure 3 and Appendix Figure 9 showcase our model's ability to maintain high quality even under large camera variations, a key aspect of flexible control.\n* **Qualitative Showcase of Trajectory Diversity**: Finally, we point to the gallery of results in Appendix Figure 11, which provides extensive qualitative evidence of our model's performance across a wide array of different camera paths, further underscoring its versatility.\n\nBy consolidating these results into a focused analysis, we believe we have now provided clear and compelling support for our claim of flexible camera control. We thank the reviewer again for prompting this important addition to our paper.\n\n#### **Weakness 3: Discussion of Dataset Generation Pipeline**\n\nWe thank the reviewer for this constructive suggestion. We agree that our data generation process is a crucial aspect of our work that warrants a more detailed discussion. Our data generation methodology is guided by the principle of **maintaining consistency between training and inference conditions**. During inference, our model is designed to handle incomplete views that are rendered from an optimized 3DGS scene with well-posed geometry. Adopting a simpler approach, such as directly generating training data from a single-shot depth estimator, would introduce significant artifacts (as shown in Figure 6 of our paper), creating a domain gap between the training data and the inference conditions. To avoid this discrepancy, our pipeline first creates a high-quality 3DGS scene from real views. From this clean 3DGS, we then extract accurate depth maps, which are back-projected to form incomplete 3D scenes. Finally, we render \"incomplete\" views from the scene. This process ensures that our training data closely aligns with the conditions our model will encounter during inference.\n\nNevertheless, we wish to emphasize the importance of the other components in our framework. As quantitatively demonstrated in our newly added quantitative ablation study (see response to reviewer v55n), our trained V2V model and our unique framework design each contribute significantly and independently to the final performance. For instance, the comparisons between configurations **(1)** & **(4)** and **(2)** & **(4)** in the table isolate and confirm the substantial impact of our V2V model and framework, respectively.\n\nWe also acknowledge the valuable suggestion regarding the experimental analysis of the dataset generation. While we are keen to conduct this analysis, the rebuttal period does not provide sufficient time to construct a new training dataset using a single-shot depth estimator like MASt3R and complete the subsequent V2V model training. Therefore, we are currently unable to include this specific experiment in the ablation study. We sincerely apologize for this omission and commit to incorporating a thorough analysis of this aspect in the final version of the manuscript.\n\n\n#### **Question 1: Camera Trajectory as Input**\n\nThank you for your sharp observation and for pointing out this confusing statement. We sincerely apologize for the lack of clarity.\n\nThe camera trajectory `c` is used exclusively to render the incomplete video `y` (as described in Ln 164), which then serves as the input for the V2V model. The V2V model itself does not take `c` as a separate, direct input.\n\nThe statement on Ln 122 was poorly phrased and was the source of this misunderstanding. We intended to convey that the control over the camera path is implicit in the input video, rather than an explicit condition.\n\nWe will correct this in the revised manuscript. The sentence on Ln 122 now reads: **\"The V2V model enables flexible control over camera trajectories hidden within input incomplete videos to generate novel views.\"**\n\n#### **Question 2: Only One Camera Trajectory?**\n\nThank you for your question, which helps us clarify the flexibility of our method. We apologize if the wording on Line 124 caused any misunderstanding.\n\nThe trajectory mentioned in Line 124 is not the only one used across all experiments. The trajectory described on Line 124 is a specific example for generating 360-degree scenes.\n\nOur method is designed to be general and is not constrained to any single type of camera path. As shown in other experiments, particularly in Figure 4 and Figure 5, our model successfully handles more diverse and arbitrary camera trajectories to complete generation tasks.\n\n#### **Question 3: Figure 4 Output and \"Novel View Synthesis\"**\n\nThank you for pointing out this ambiguity regarding “novel view synthesis”. We sincerely apologize for the confusion. Figure 4 shows the direct output of our proposed V2V (video diffusion) model, not the views rendered from the optimized 3DGS scene. Our use of the term \"novel view synthesis\" was intended to be consistent with prior work, specifically ViewCrafter [1], where it describes the task of generating new views as a video sequence.\n\nTo resolve this ambiguity, we will revise the caption of Figure 4 to state that the results are videos generated by our video diffusion model to avoid misunderstanding.\n\n[1] Yu W, Xing J, Yuan L, et al. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis[J]. arXiv preprint arXiv:2409.02048, 2024.\n\n#### **Question 4: How the Incomplete Video `y` is Obtained**\n\nThank you for your question. To clarify, your understanding that `y` and `x` share the same camera trajectory is correct. However, the 3D structure used to render `y` is built using only the information from the first frame of the video, as stated on Ln 172. We create a sparse point cloud from this single frame, which naturally lacks information about parts of the scene outside the initial view. The \"incomplete video\" `y` is the result of rendering this sparse structure along the full camera path.\n\nConsequently, `y` is guaranteed to contain significant missing regions and artifacts compared to the complete ground truth video `x`, making it a suitable input for our V2V task. \n\n\nWe sincerely appreciate your time and insightful feedback. Your comments have been invaluable in improving our manuscript. We have carefully addressed all your concerns through additional analyses and clarifications, which we believe will strengthen the paper. While some suggested experiments will require more time than is available for this rebuttal, we are confident the current modifications have substantially improved our work."}]

Author rebuttal:

Meta-review: The paper presents a solid method with thorough experimental validation and well-prepared datasets. Reviewers acknowledge that most concerns have been addressed, and ablations demonstrate the impact of key pipeline components. Standard metrics are sufficient to evaluate camera controllability and novel view synthesis, and the method is robust without requiring additional components. Overall, the paper makes a meaningful contribution, and I therefore recommend acceptance.
Decision: Accept (poster)