One2Scene: Geometric Consistent Explorable 3D Scene Generation from a Single Image
Authors: Pengfei Wang
Liyi Chen
Zhiyuan Ma
Yanjun Guo
Guowen Zhang
Lei Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: scene generation
multi-view diffusion
feedforward Gaussian Splatting
Primary area: generative models

Abstract: Generating explorable 3D scenes from a single image is a highly challenging problem in 3D vision. Existing methods struggle to support free exploration, often producing severe geometric distortions and noisy artifacts when the viewpoint moves far from the original perspective. We introduce One2Scene, an effective framework that decomposes this ill-posed problem into three tractable sub-tasks to enable immersive explorable scene generation.
We first use a panorama generator to produce anchor views from a single input image as initialization. Then, we lift these 2D anchors into an explicit 3D geometric scaffold via a generalizable, feed-forward Gaussian Splatting network. 
Instead of treating the panorama as a single image for reconstruction, we project it into multiple sparse anchor views and reformulate the reconstruction task as multi-view stereo matching, which allows us to leverage robust geometric priors learned from large-scale multi-view datasets.
A bidirectional feature fusion module is used to enforce cross-view consistency, yielding an efficient and geometrically reliable scaffold.
Finally, the scaffold serves as a strong prior for a novel view generator to produce photorealistic and geometrically accurate views at arbitrary cameras. By explicitly conditioning on a 3D-consistent scaffold to perform reconstruction, One2Scene works stably under large camera motions, supporting immersive scene exploration. Extensive experiments show that One2Scene substantially outperforms state-of-the-art methods in panorama depth estimation, feed-forward 360° reconstruction, and explorable 3D scene generation. Project page can be found at: https://one2scene5406.github.io

Published as a conference paper at ICLR 2026
ONE2SCENE: GEOMETRIC CONSISTENT EXPLORABLE 3D SCENE GENERATION FROM A SINGLE IMAGE
Pengfei Wang", Liyi Chen", Zhiyuan Ma, Yanjun Guo, Guowen Zhang, Lei Zhangi The Hong Kong Polytechnic University
pengfei. wange connect. polyu. yu. .k, calzhang@comp. pol yolly..eoo edu. hk "Equal contribution. CCorresponding author.
Project page: https wwtps////oeeeeeeeeeeeeee git.bb.itttubibooom
ABSTRACT
Generating explorable 3D scenes from a single image is a highly challenging problem in 3D vision. Existing methods struggle to support free exploration, often producing severe geometric distortions and noisy artifacts when the viewpoint moves far from the original perspective. We introduce One2Scene, an effective framework that decomposes this ill-posed problem into three tractable sub-tasks to enable immersive explorable scene generation. We first use panorama generator to produce anchor views from a single input image as initialization. Then, we lift these 2D anchors into an explicit 3D geometric scaffold via generalizable, feed-forward Gaussian Splatting network. Instead of treating the panorama as a single image for reconstruction, reformulate the reconstruction task as multi-view stereo matching, which allows us leverage robust geometric priors learned from large-scale multi-view datasets. A bidirectional feature fusion module is used to enforce cross-view consistency, yield- ing an efficient and geometrically reliable scaffold. Finally, the scaffold serves as a strong prior for a novel view generator to produce photorealistic and geometrically accurate views at arbitrary cameras. By explicitly conditioning on 3D-consistent scaffold to perform reconstruction, One2Scene works stably under large camera motions, supporting immersive scene exploration. Extensive experiments show that One2Scene substantially outperforms state-of-the-ard methods in panorama depth estimation,
project it into multiple sparse anchor views and
360 reconstruction, and explorable 3D scene generation.
Wonderjourny
DreamScene360
Ours
Figure 1: Comparison on large-viewpoint novel view synthesis. Existing methods such as Wonder- journy Yu al., 2023) and Dreamscene360 (Zhou et al. (024) exhibit clear geometric distortions and artifacts, while our method generates photorealistic and geometrically accurate novel views. The input image highlighted by red bounding box. The other images represent the novel views.

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INTRODUCTION
The increasing demand for high-quality 3D content is reshaping the landscape of video games, visual effects, mixed reality, and 3D scene understanding (Wang et 2024a). making 3D generation a highly active research topicm (Valevski et al., 2024 Adamkiewiez et al. 2022 Martin-Bruall et al.
2021 Ye et al. 2024b, Chen et al. (025a). Reconstruction-basse methods like Neural Radiance Fields (NeRH) (Mildenhall et al. (020) and Gaussian Splatting (GS) (Kerbl al.) 2023) have achieved remarkable results, but they typically require hundreds or even thousands of input images. Although
sparse-view reconstruction approaches alleviate this requirement (Wang et al., 2023, Yang et al.
2023
Yu et al. 2024a; Charatan et al., 22224; Liu et al., 2024c by Wu et | [2024a] Szymanowicz ct al. 2024b), these methods struggle with large viewpoint extrapolation and fail to generalize to
unseen regions. In stark contrast, generative view synthesis (Liu et al. 2023, Sargent al. 2024
Liu et al., 2024a Yu et al., 2024b Li et al. 2025b) is emerging as a significant advancement in 31)
content creation, it can generate plausible content in unobserved regions (Shi et al. 2024 Zhou
ct al.
2025 Szymanowicz et al. 2025
Although object-level 3D generation (Liu al. 2023 Sargent al. 2024 Ye al. 2024b) has
achieved rapid progress, generating an explorable 3D scene from a single e image remains a significant challenge. One of the key challenges is how to maintain 3D geometric consistency and visual quality under large viewpoint changes and long-term generation. Some methods leverage pre- tre-trained video generation models (Brooks et al. 2024, Xing et al., 2024; Hong et al. 2022 Yang et al. 2024 to create 3D-aware sequences (Liu et al., 2024a di Yu et al. 2004bb Chen et al. [0244) Sun et al. 2024 Liang et al. 2024), but they often suffer from geometric inconsistency and loop-closure consistency, Panorama-based pipelines such as Dreamscene360 (Zhou et al. 2024) and DreamCube (Huang et al.. 2025) attempt to convert panoramas into 3D scenes, but their ability to support broader exploration is very limited, as shown in Figure I (a). Although navigation and inpainting aapaeeting-basedd methods (Chung ctt al. 2023: Yu et al. 2023 Höllein et al., 2023) enable the generation of more expansive scenes, their iterative nature often causes global semantic drift. Furthermore, cumulative errors often result in stretched or distorted geometry, as shown in Figure (b). These limitations highlight the need for a new approach that can produce geometrically accurate and photorealistic scenes from a single image while supporting broad exploration.
To achieve the goal mentioned above, in this paper we introduce One2Scene, a novel framework that systematically decomposes explorable 3D scene generation into three distinct, yet more manageable subtasks. First, overcome the profound information deficit of a single image, we generate a set of anchor views for global coverage using a panoramic cubemap representation. Note that these anchor views alone are insufficient to create truly explorable scene, as shown in Figure [ (a). Full exploration requires synthesizing high-quality novel views from arbitrary viewpoints, while how to ensure 3D consistency presents a significant hurdle. To this end, we introduce a powerful and efficient prior that encodes both geometry and appearance to stably constrain the generative process. Specifically, we reformulate the problem of monocular panoramic depth estimation as a multi-view stereo matching problem across extremely sparse anchor views, and lift the 2D anchor views into an explicit 3D geometric scaffold using a feed-forward 3D GS model. Such a design not only ensures the high efficiency of our feed-forward model but also critically enables us leverage robust geometric priors learned from large-scale multi-view datasets. To further enforce geometric consistency across anchor-view boundaries, we introduce bidirectional fusion module. As a result, our ferw-forward model can reconstruct a geometrically accurate, high-quality 3D scaffold in 0.5 seconds.
The constructed explicit geometric scaffold provides strong priors for both geometry and appearance to guide the final novel view synthesis. To effectively utilize this scaffold, we introduce novel DuR training strategy. Unlike common refinement models that use channel-wist conditional injection (Wu et al. 2025). our strategy effectively fuses information from the high-quality input view with the coarse yet geometrically-rich views rendered from our scaffold. These combined conditions then guide the generation process arbitrary camera views via a global 3D-aware attention mechanism. Our experiments demonstrate that this design significantly enhances the model's ability to leverage the priors provided. By grounding the generation process in a consistent 3D representation, the final results of our One2Scene model are not only photorealistic but also exhibit superior multi-view consistency, as demonstrated in Figure (c).
Our contributions can be summarized as follows. First, we introduce a powerful feed-forward 3D GS model with bidirectional fusion module construct a high-quality 3D scaffold by reformulating
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the monocular panoramic depth estimation into multi-view stereo problem. Second, we present a seafold-ggided synthesis method to utilize explicit geometric and appearance priors from any target view, which robustly grounds the final rendering and resolves the geometric ambiguities inherent in single-image generation. Finally, we demonstrate that our proposed One2Scene sets a new state-of-the-art on explorable 3D scene generation, achieving superior photorealism and geometric accuracy, particularly under significant viewpoint shifts.
2 RELATED WORK
3D Scene Reconstruction. While differentiable rendering techniques such as NeRF (Mildenhall et al. 2020) and 3DGS (Kerbl et al., (023) have achieved remarkable results, they primarily tailored for per-scene optimization requiring dense input views, a constraint that hinders their deployment in real-world scenarios. In response, the research community has introduced various methods for
sparse-view reconstruction (Wang et | 2023] Yang et al. [023] | Yu et al. 2024a; Charatan et al.)
2024 Liu et al. b0)4c(b] bb Wu et al. [2024a] Szymanowicz et | 2024b). Concurrently, generalizable
Teed-forwal models (Charatan et al., 2002, Chen et al., 2025b b Szymanowicz et al.) 2024b a Wewer
al. 2024, Xu et al. 2025 Ye et al. [2024]] Hong al. 2024| Tang et al.) (024) have garnered
sigmificatt attention for their ability to directly infer 3D representations from sparse inputs without
per-instance optimization. Despite these advancements, these methods share critical bottleneck: a lack of extrapolation capability, resulting in an inability to plausibly render unobserved regions.
Video Eeftusion-baetee 3D Scene Generation. Recent video generation models (Brooks et al.) 2024
Xing et al. 2024 Hoogg et al. [2022] Yang et al. 2024 Wan et al. (0255) have shown great potential to generate 3D-aware sequences. These models can naturally serve as 3D scene generators when
camera poses
controllable (Guo et al. 2024 Wang et al. 2024c Melas-Kyriazi et al.) 2024
Voleti et al., 2024; Liang et al. 2024). To enhance 3D consistency, contemporary approaches like ReconX (Liu et al. (0224a), ViewCrafter (Yu et al., 2024b). and VMem (Li et al. 2025a) integrate explicit 3D geometric priors into their frameworks, leveraging robust reconstruction backbones such as DUSt3R (Wang et al.] 2024b) and CUT3R (Wang al., 20256). However, despite these advancements, such methods remain limited in large-scale explorable scene generation, where the accumulation of reconstruction errors leads to geometric collapse.
Image Diffusion-based 3D Scene Generation. Several innovative investigations (Liu et al. 2023
Wu et al., 2024b) Sargent al. 2024; Höllein al., 2024, Seoo al., 2024, Shi et al. 2024: Wang & Shi 2023] Shi et al. [023] Liu et al. 2024c) have incorporated camera pose information into pre-trained T21 models to generate novel views. Within this category, two key strategies have emerged for generating explorable scenes from a single image. The first strategy employs pose-conditioned view synthesis. Methods such as SEVA (Zhou et al., (025) and CAT3D (Gao al. 2024) leverage camera pose information to guide the generation of novel views, demonstrating impressive scene- level results. However, when applied to single-image inputs over extended camera trajectories, these methods struggle to maintain long-range geometric consistency and visual coherence, often resulting in accumulated errors and semantic drift that compromise global scene structure. The second strategy relies on iterative navigation and inpainting Pu al. 2024 Chung al., 2023
Yu et al. 2023 Höllein et af.) 2023). One notable example, Pano2Room (u et al. (2024). builds the scene sequentially by navigating through space and inpainting unseen areas. Although can produce plausible indoor results, this iterative framework is inherently prone to accumulating geometric and appearance errors over time, compromising global scene consistency. A second limitation is its design, which incorporates strong indoor priors that restrict its generalization to outdoor scenes and diverse visual styles.
In contrast to these sequential approaches, our One2Scene framework introduces a novel scaffold- guided paradigm. It decomposes the ill-posed sinle-image-to-seeee problem into more manageable subtasks, achieving superior geometric fidelity and photorealistic quality. By first generating a globally consistent 3D scaffold a single, feed-forward pass, our One2Scene method establishes robust geometric and semantic foundation for the entire scene. This holistic global prior directly counteracts the eтror accumulation inherent in sequential methods like pose-conditioned synthesis and iterative inpainting. Consequently, our approach is not only more geometrically consistent but also significantly more general than specialized methods like Pano2Room, demonstrating superior performance across both indoor and outdoor environments.
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(a) Panorama Generation
(b) Feed-forward 3D Geometric Scuffold
Panorama Gencrator
View Projectio
Feed forward 3D GS Model
Input
Anchor Views
Depths
Params,
Panoraima 3DGS
Panorama
Rendeing
(c) Scaffold- Guided
Turpet
Packer
Andher
All Fennks
Phatker
Target View
Readerings
Eaits
Lelk&
Generttd
NVS Generator
Placker Ends
All B-taado
Figure 2: Overview of One2Scene. Our method consists of three stages: (a) an anchor view generation stage to establish an initial 360-degree representation, (b) a feed-forward Gaussian Splatting stage to construct an explicit 3D geometric scaffold, and (c) synthesis stage that leverages the scaffold information to produce high-quality novel views. The pipeline enables geometrically consistent and photorealistic novel view synthesis from single input image.
METHODOL.OGY
This section details One2Scene framework, which generate explorable 3D from single image by decomposing this ill-posed problem into a sequence of manageable sub-tasks, as illustrated Figure First, to overcome the severe information deficit, generate a panorama cover the global scene. Second, we obtain a set of anchor views from the panorama and introduce feed-forward 3D GS model to lift these 2D anchor views into an explicit 3D geometric scaffold. Finally, with the strong geometric and appearance priors provided by the 3D scaffold, a synthesis network is used generate photorealistic and consistent novel views from arbitrary camera poses.
3.1 PANORAMA GENERATION
Generating explorable 3D scenes from single image is highly challenging problem, often resulting in pronoumced semantic drift and geometric inconsistency across long-range novel views. address this challenge, adopt progressive approach that first expands visual information content and subsequently establishes a robust geometric foundation. We employ specialized image-to- panorama generation model transform the limited input view into 360 panoramic representation. This representational choice motivated by two primary considerations. First, the comprehensive field of view provides more visual cues that facilitate subsequent globally consistent scene generation. Second, compared to direct arbitrary novel view synthesis, panoramic image generation with single image as input is more well-posed computational task. particular, we employ Hunyuan- Pano-DiT DiT (Wang et al.) al.]| [2025c). which demonstrates exceptional generalization capabilities acquired through training on extensive large-scale datasets, to generate panoramic image.
image-to-panorama
3.2 FEED-FORWARD 3D GEOMETRIC SCAFFOLD
Although the panorama generated from the initial stage provides global coverage, it remains 2D representation estimated from single viewpoint and lacks explicit 3D information. Maintaining geometric consistency when synthesizing with large viewpoint changes and long sequences remains fundamental challenge in explorable scene generation. To this end, we introduce a novel feed-forward 3D GS model to predict a set 3D Gaussian parameters (μ 0g, Σ,, c.), = x for each pixel in the generated panorama. This process provides the scene with explicit 3D information, thereby ensuring global geometric consistency.
Anchor View Projection. Accurate depth estimation is the comerstone of this model, as inaccurate depth can introduce severe rendering artifacts. Although significant progress has been made in depth estimation from single panoramic image (Ai al. 2023 Wang & Liu 2024 Pintore et al.
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2023), this task remains highly challenging, A key difficulty lies in the lack of large-scale datasets comparable to those available for perspective images, limiting the generalization ability of panoramic depth estimators. To achieve robust depth estimation, we propose to reformulate the problem of monocular panoramic depth estimation as a multi-view stereo matching problem. Specifically, we first project the 360" panorama into set of six perspective cubemap views, which serve as the input anchor views for our model. This strategy allows us to leverage powerful geometric priors learned from large-scale multi-view datasets. We choose to use cubemaps because they provide the most compact perspective representation of the panoramic scene, ensuring high efficiency. To facilitate correspondence matching across views, we expand each cubemap's s Field of View (FoV) to 95%, creating 2.5" overlap at adjacent view boundaries. For further details, please refer to ??.
Bidirectional Fusion Module. Although 2.5-degree overlap established between adjacent anchor views, the correspondence remains extremely sparse. Existing multi-view stereo models like VGGT (Wang et al., 2025a), which rely on substantial inter-view overlap, suffer from significant performance degradation in such scenarios. To address this limitation, propose novel architectural modifications to VGGT to explicitly enforce cross-view consistency and improve the robustness of depth estimation. Specifically, DPT head of VGGT to promote cross-view depth consistency. This mechanism establishes geometric correspondence across views while preserving view-specific details.
integrate bidirectional fusion mechanism into the pre-traine pre-trained
To effectively handle overlapped regions, we introduce a Cube-to- Cube-to- Equirrecttanalllrr (C2F) transforma- tion module that projects the dense feature maps F; from the six anchor views into unified equirect- angular latent. Subsequently, these equirectangular features are fused using convolutional layer He- Then, the fused features F, are transformed back the cubic space via an Equirectangulartto-CC (E2C) module and merged with the original anchor view features through residual connection. The finally updated feature for each view, F", is computed as follows:
F. = (C2E( D), F =; + E2C(F
This bidirectional transformation and fusion mechanism aligns features overlapped regions to achieve geometric consistency via C2E/E2C transformations, while using residual connections to maintain view-specific details simultancously. For further details, please refer to ??.
Gaussian Parameter Prediction Heads. For each pixel, the Gaussian center μ is computed by unprojecting the predicted depth into 3D space using the camera intrinsics: μز = K 'ud + A, where K denotes the camera intrinsic matrix, indicates the predicted positional offset. To predict the remaining Gaussian parameters (opacity, covariance, and color), we employ an additional prediction head based the DPT architecture. Following NoPosplat (Ye et al. 2024a), this prediction head takes both VGGT features and the RGB image as inputs. The direct pathway from RGB images complements VGGT's high-level lemantic-focuser features by preserving essential fine textural details.
(uz Ny. 1) represents the pixel coordinates, and A R
Training. The feed-forward 3DGS model is trained using a composite loss function, which includes rendering loss and depth loss. The rendering loss is a combination of the Mean Squared Error (MSE) and the LPIPS perceptual loss (Johnson et al.) (016)). while the depth loss is the Icale-Invariant Logarithmic (SIL.og) loss (Figen et 2014). The model is trained on a collection of four datasets: two synthetic datasets, Structured3I (Zheng et al., 2020) and Deep360 (Li et al. [2022), and two real- world datasets, Matterport3D (Chang al. (017) ) and Stanford2D3D (Armeni et al. (017). Through this training regimen, our feed-forward 3DGS model demonstrates precise geometric modeling capabilities and robust generalization across indoor, outdoor, and even stylized scenes.
3.3 3D SCAFFOLD GUIDED NOVEL VIEW SYNTHESIS
In the final stage of our pipeline, we leverage the 3D geometric scaffold to generate a fully explorable 3D scene. In particular, we propose to transform the task of novel view synthesis from a single view to the problem of synthesis conditioned on the set of anchor views:
p Tist
Tanchor
p anchar p!!)
However, the above formulation remains limited since the anchor views are all observations from a single point in the space, and they lack the explicit scale and geometric information required for robust 3D understanding. Our 3D geometric scaffold, with its precise geometric modeling capabilities,
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overcomes this limitation by enabling the rendering of novel views from arbitrary viewpoint. These rendered views contain rich geometric and appearance information. Therefore, they can serve as powerful conditions to guide the synthesis of novel views, significantly enhancing their realism and consistency. Although these rendered views may exhibit artifacts or occlusions (e.g. black holes) for large viewpoint changes, they still retain substantial amount of useful structural information, owing to our model's ' accurate depth estimation. This insight allows us to further reformulate the synthesis problem as follows:
Tanchor
P pucchrr
prnder .p
where view gender is rendered from the scaffold in the camera pose of the target view 1
Dual-LoRA Training. It is challenging task to manage two distinct types of conditions in the syn- thesis process: the high-quality anchor views, which offer pristine appearance but are geometrically ambiguous, and the rendered views, which provide strong geometric priors but may contain artifacts. To effectively guide the synthesis using both conditions, we need to process these heterogeneous signals. Inspired by MMDiT (Esser et al.] 2024). which uses separate encoders for different modali- ties, such as text and images, before fusing their features for self-attention, we propose a Dual-LoRA training strategy. Built upon the SEVA architecture (Zhou et al. 2025), our approach employs two different LoRA modules to process the anchor view and the rendered view independently, as shown in Figure (c). The features from both conditions are then integrated with the noisy latent represen- tation through 3D attention mechanism. Our experiments confirm that this method demonstrates significantly stronger learning capabilities compared to a naive approach of simply concatenating the rendered view with the noise latent.
Memory Condition. To ensure temporal and spatial consistency when generating large number of frames for continuous 3D scene, we introduce an additional memory condition during inference. This condition is a previously generated frame selected from a memory bank, which has the closest average camera pose to the current target frame. The synthesis problem is thus further refined to:
p (I ranchor anchhr
rrnder Imem
mem
tgt
(4)
This memory-guide approach effectively preserves visual consistency, particularly when synthesizing content in occluded regions.
Training Data Construction. To assemble a dataset for supervised training, we perform sparse 3D reconstructions on the DL3DV (Ling et al., 2023) and RealEstate 10K (Zhou al. 2018) datasets using the pre-trained feed-forward 3DGS model MVSplat (Chen et al., 2025b). This strategy is intentionally employed to simulate the artifacts and holes that arise in rendered views when the reconstruction is based on sparse input viewpoints. By using the camera trajectories inherent to these datasets, we sample novel views that exhibit significant viewpoint deviations. Training pairs are subsequently formed, each comprising a ground truth image and its corresponding view rendered from the sparse 3D reconstruction at the identical camera pose.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Implementation Details. In the panorama generation stage, we employ Hunyuan-Pano-Da (Wang et al. 2025c) as the generator. The feed-forward 3DGS model is trained for 80,000 iterations using the AdamW optimizer. We set the learning rate of the VGGT backbone to 2e-5, and set the learning rate to 2e-4 for all other modules. In the final stage, the 3D scaffold-guie -uidded novel view synthesis model is trained for 40,000 iterations using the Adam optimizer based on SEVA (Zhou et al. 2025). with batch size of 16 and learning rate of 1.25e-5.
Experiments Setup. To more comprehensively evaluate our proposed One2Scene model and demonstrate its effectiveness and advantages, we conduct the following experiments. (1) First, we benchmark our One2Scene model against the SOTA 3D scene generation models in producing high-quality, explorable 3D scenes. (2) Second, we evaluate the key component of our One2Scene model, i.e., the geometric accuracy with the SOTA methods. Its depth estimation performance is also evaluated on standard panorama depth estimation benchmarks. (3) Third, we conduct a series of ablation studies to dissect the effectiveness of our design of One2Scene.
360 reconstruction network, by comparing its quality, efficiency, and

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Evaluation Metrics. We evaluate the quality of our generated scenes across three key dimensions. (1) Visual Fidelity. We measure visual quality using two no-reference image quality assessment metrics: NIQE (Mittal et al. 2012) and Q-Align (Wu et al. 2023). (2) Semantic Consistency. We measure the semantic consistency between the initial image and the novel views using CLIP-I score (Hessel et al. 2021). (3) Geometrie Consistency. We evaluate geometric stability by first estimating the camera poses of the generated views with a pre-trained VGGT model. These estimated poses are then benchmarked against the ground-truth camera trajectories to compute Rotation Error (RotError) (He et al., 2024), Camera Motion Consistency (CamMC) (Wang et al. 2024c), and Translation Error (TransError) (He et al. 2024). More details of our evaluation protocol are provided in ??.
4.2
MAIN RESULTS
4.2.1
EXPLORABLE 3D SCENE GENERATION
To establish a rigorous evaluation protocol in the absence of a standard benchmark for explorable 3D scene generation, we adapt the WorldScore benchmark (Duan et al. 2025), which is originally proposed for short-sequence 3D scene evaluation. To ensure a comprchensive assessment, we sample 40 scenes spanning four diverse static scene categories: indoor-real, indoor-ttlized, outdoor-real, and outdoor-stylized (10 per category). This diverse benchmark allows us to thoroughly test the robustness and quality of the generated 3D scenes from single-view inputs.
Results. We compare One2Scene with DreamScene360 (Zhou et al. 2024), WonderJourney Yuu al. 2023), VMem (Li et al., 2025a) and SEVA (Zhou et al. 2025). Quantitative results are reported in Table I For methods that accept camera-conee conditioeed novel view synthesis, we additionally evaluate geometric consistency. Since DreamScene360 and WonderJourney do not produce fully explorable scenes (as shown in Figure we can only perform qualitative comparisons with VMem and SEVAin, as shown in Figure 3 We also condition VMmm and SEVA on the anchor views produced in our One2Scene method, and denote the corresponding methods as VMem+ and SEVA+.
Semantic and Appearance Consistency. As demonstrated in Figure 3 SEVA and VMem often hallucinate content in unohserved regions, leading semantic inconsistencies. Our 3D scaffold, however, preserves global semantic coherence. This advantage is validated by our quantitative results in Table [ our One2Scene achieves superior NIQE (4.43) and Q-Align (4) scores, and its CLIP-I score (89.95) markedly surpasses those of SEVA (87.82) and VMem (75.80).
Scale Ambiguity and Drift. As noted by Zhou et al. (2025) SEVA, the single input image makes SEVA suffer from scale ambiguity issues. This manifests the distortion of object size and physically implausible geometric artifacts, such as cameras penetrating through walls (see Figure B). Even conditioned on our anchor views, SEVA+ and VMMe+ remain unable to effectively resolve the scale drift problem. This fundamental limitation stems from the lack of relative translation information in anchor views, which prevents the model from inferring unified global scale. In contrast, our method explicitly constructs a 3D scaffold that provides robust scale constraints, effectively mitigating the scale ambiguity issue and producing physically plausible results.
Geometric Stability. Existing methods often struggle to maintain long-term geometric stability. SEVA, for example, lacks a persistent geometric representation, causing inconsistent reconstructions in loop-closure scenarios (c.g., frame 78 vs. 255 in Figure VMem attempts to enforce consistency via online reconstruction with CUT3R, but this strategy highly susceptible to vicious cycle of error accumulation: generated low-quality frames destroy the geometry, which in turn provide wrong guidance for subsequent frames, leading to catastrophic failure. In contrast, our pre-built 3D scaffold provides a stable geometric prior, effectively preventing error propagation. This advantage is substantiated by the quantitative results: our method achieves a score of 0.389 in CamMC, significantly outperforming VMem (0.998, see Table D]
The above results highlight the superiority of our three-stage design of One2Scene, which systemati- cally addresses the global semantic inconsistency, scale ambiguity, and geometric instability. More results can be found in ?? and our anonymous project page. Camera
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SEVA
SEVA+
VMem
VMMm
Frame 7
Frame 78
Frame 93
Frame 124
Frame 152
Frame 193
Frame 255
Frame 282
Figure 3: Qualitative comparison. Our method retains compelling visual quality and generates
plausible continuations of the scene, even under large viewpoint change.
Table I: Quantitative comparisons for 3D scene generation.
Methods
NIQE
Q-Alignt
CLIP-I
TransErr
RotEr 1 CamMC
DreamSeene Zhou et al. (2024)
8.40
1.91
74.24
WonderJourney Yuu 2025)
4.97
77.92
SEVA Zhou
4.53
3.20
87.82
0.460
0.165
0.558
SEVA Zhou
2025
Anchor
4.45
3.45
88.70
0.422
0.116
0.460
VMmm Li
al.
7025a
6.86
2.95
75.80
0.573
0.569
0.998
VMmm
Li ct al.
20258
Anchor
5.23
3.04
81.33
0.613
0.426
0.887
One2Scene (Ours)
4.43
4.13
89.95
0.326
0.107
0.389
4.2.2
FEED-FORWARD 360 RECONSTRUCTION
This section validates the core advantages of our feed-forward 3DGS network, cornerstone of our pipeline. We demonstrate superiority reconstruction quality, computational efficiency, and geometric accuracy compared to SOTA methods.
Reconstruction Quality. conduct a direct comparison with the SOTA method, AnySplat (Tang al., 2025). Since both methods extensions of the VGGT model, this shared foundation ensures a fair evaluation. As shown in Figure 4 AnySplat's reconstruction fails with only 6 sparse views. This is because it predicts erroneous depth map. which results in distorted geometric scene. Even when 20 densely tangent patches with substantial overlap are projected from a panorama, its performance remains sub-par, suffering from severe artifacts in drastic viewpoint changes. In stark contrast, our model constructs a high-quality and robust 3D geometric scaffold even from sparse inputs. Although large rotations can introduce minor local artifacts due to occlusion, the underlying geometric foundation remains stable, providing crucial priors for the subsequent generation task.
Table Comparison on the 3D scene generation performance by replacing our feed-forward 360 reconstruction network with AnySplat.
Methods
NIQEI
Q-Alignt
CLIP-1"
TransErr
RotEir L
CamMC
SpySplat (Jiang et al. 2025
4.96
3.61
81.96
0.332
0.367 0.107
0.616
Ours
4.43
4.13
89.95
0.326
0.389
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Table 3: Comparison of depth estimation on Matterport3D and Stanford2D3D datasets.
Matterport3D
Stanford2D3D
Methods
AbsRel
6
6²7
8,
AbsRel.
627
da7
BiFuse Wang al.
2020
0.2048
84.52
93.19 19
96.32
0.1209
86.60
95.80
98. 60
UniFuse
Drnng cl
2021
0.1063
88.97
96.23
98.31
0.1114
87.11
96.64
98.82
HoHoNet
Sun el
2020
0.1488
87.86
95.19
97.71
0.1014
90.54
96.93
98.86 86
Bilfuset+
Wang el al.
2022
87.90
95.17
97.72
87.83
96.49
98 84
ACDNet Zhuang et 2022
0.1010
90.00
96.78
98.76
0.0984
88.72
97.04
98.95
PanoFormer (Shen
2022
0.0904
88.16
96.61
98.78
0.1131
88.08
96.23
98.55
HRDFusc
(Ar
2025
0.0967
91.62
96,69
98.44
0.0935
91.40
97.98
99.27
EGiFormr Yun ot al. 20233
0.1473
81 81.58
93.90
97.35
0.1528
81.85
93.38
97.36
Elite360DD
& Wang 2024
0.1115
88.15
96.46
98.74
0,1182
88.72
96.84
98 92
Depth Wnyweere
[aagg & LTu. 2024
91.70
97.60
99.10
0.1180
91.00
97.10
Ours (Zero-shot)
0.1070
88.97
96.51
98.61
0.0675
95.20
98.53
99.30
Ours (Finetune)
0.0391
98.09
99.41
99.74
0.0444
96.95
98.85
99.44
The importance of our scaffold is further confirmed by the experiment in Table 2 replacing our reconstruction module with AnySplat causes significant degradation in final generation quality.
Computational Efficiency. Using six sparse views, our model reconstructs a high-quality scaffold in 0.5 seconds H20 GPU, marking a 5.6x speedup over AnySplat, which relies dense view set and requires 2.8 seconds. The inference time is further slashed to only 0.1 seconds when using more powerful NVIDIA H100 GPU.
To quantitatively assess the geometric accuracy of our model, we evaluate its depth estimation performance against SOTA methods on the Matterport3D and Stan- ford2D3D datasets. As detailed in Table the results are compelling: our model, when applied in a zero-shot setting, surpasses compared approaches on the Stanford2D3D dataset. This result indicates that our method effectively inherits and transfers geometric priors from the foundational VGGT model. Furthermore, when our model fine-tuned on the Matterport3D and Stanford2D3D datasets, it demonstrates exceptional performance, boosting the AbsRel metric by over 50%. This further underscores the powerful geometric modeling capabilities of our reconstruction model.
Accurate Depth Estimation.
4.3 ABLATIONS AND ANALYSIS
Given limited space, we provide comprehensive ablation studies in the Appendix, featuring in de-thphh analyses of our Dual-LoRA training methodology, memory condition mechanism, and bidirectional fusion module (see ??). We also provide detailed quantitative evaluation results for our generation model on the DI.3DV dataset (see ??).
CONCLUSION AND LIMITATIONS
In this paper, we introduced One2Scene, novel and effective framework for generating fully ex- plorable 3D scenes from single image. We addressed the critical challenge of geometric distortion and artifact generation in existing methods when there were large viewpoint changes. Our core contribution lied in the decomposition of this ill-posed problem into three tractable subtasks: initializ- ing sparse anchor views via panorama generator, lifting them into explicit and geometrically reliable 3D scaffold by a feed-forward GS network, and finally, leveraging the scaffold as strong prior for photorealistic novel view synthesis. Our extensive experiments validated that One2Scene substantially outperformed state-of-the-ar methods in explorable 3D scene generation.
Limitations. While our approach significantly improves 3D consistency across long sequences and large viewpoint changes, the generated views may contain subtle inconsistencies. Similar to further enhance geometric consistency through reconstscntt
CAT3D (Gao et 2024), processing. Please see the "Result Gallery" on our anonymous project page. In future work, we plan to construct larger-scale datasets further improve model's performance and robustness.

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Published as a conference paper at ICLR 2026
AnySplat
wingybaa (20 views
Ours
Figure Ablation study on reconstruction performance, compare the 3D scene generation quality by replacing our feedforward network with AnySplat. Top row: reconstruction results. Bottom row: generation results using our model.
ETHICS STATEMENT
This research does not involve human participants the collection of sensitive personal information. All datasets utilized this study are employed in strict accordance with their respective licensing agreements and terms of use.
The proposed methodology is designed exclusively for academic research and scientific advancement. While we do not anticipate direct harmful applications, recognize the potential for misuse if deployed without appropriate ethical considerations and safety measures. We advocate for the respon- sible application of our research contributions, emphasizing the importance of fairness, transparency, and adherence to applicable legal frameworks.
REPRODUCIBILITY STATEMENT
We have implemented comprehensive measures to facilitate the reproducibility of our research findings. The main manuscript provides thorough documentation of our proposed framework. including detailed descriptions of the model architecture, dataset preprocessing methodologies, and algorithmic implementations, Complete hyperparameter configurations and training protocols are explicitly specified to enable independent replication of our results.
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Reviews:
[{"review_id": "GGIydvCgKL", "reviewer": "Reviewer_3G7Z", "summary": "One2Scene introduces a novel three-stage framework for generating geometrically consistent, explorable 3D scenes from a single image, overcoming severe distortions and artifacts that plague prior methods like DreamScene360 and WonderJourney. First, it generates a 360° panorama using Hunyuan-Pano-DiT; second, it projects this panorama into six sparse cubemap views and lifts them into an explicit 3D Gaussian Splatting scaffold via a feed-forward network that reformulates monocular depth estimation as multi-view stereo matching, leveraging robust geometric priors from large datasets through a bidirectional feature fusion module that enforces cross-view consistency; finally, it uses this scaffold as a strong geometric and appearance prior for novel view synthesis, guided by a Dual-LoRA training strategy that fuses anchor views and scaffold-rendered views via 3D-aware attention, enabling stable, photorealistic generation under large camera motions—achieving state-of-the-art results on visual fidelity, semantic consistency, and geometric stability, with a 0.5-second reconstruction time and significantly improved depth estimation accuracy over existing methods, as validated on Matterport3D and Stanford2D3D.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1: The paper introduces a clever reformulation of monocular 3D scene generation by treating panoramic depth estimation as a multi-view stereo problem, which had not been done before.\n\nS2: The feed-forward 3D Gaussian Splatting scaffold runs in under half a second and delivers unprecedented geometric stability without iterative refinement.\n\nS3: The bidirectional fusion module and Dual-LoRA conditioning are elegant, practical innovations that significantly improve cross-view consistency and visual fidelity.\n\nS4: By enabling immersive, long-range exploration from just one image—with results that surpass prior methods in both quality and robustness—it sets a new practical standard for 3D content creation.", "weaknesses": "W1: The method relies on a proprietary panorama generator (Hunyuan-Pano-DiT) without ablation on alternatives, limiting reproducibility.\n\nW2: The 3D scaffold still shows minor artifacts in occluded regions under extreme rotations, suggesting room for post-processing or iterative refinement.\n\nW3: Evaluation on stylized scenes lacks quantitative metrics for artistic fidelity—CLIP-I and NIQE may not capture stylistic coherence.\n\nW4: No comparison to recent diffusion-based single-image 3D baselines like Bolt3D or DreamReward, which also aim for speed and realism.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "63Ok59XIDB", "reviewer": "Reviewer_Lk3Z", "summary": "The paper One2Scene proposes a three-stage framework for generating fully explorable 3D scenes from a single image, addressing the challenges of geometric inconsistency, scale ambiguity, and semantic drift common in prior generative view synthesis methods.\nThe pipeline includes:\n1. Panorama generation, expanding a single input image into a 360° representation using Hunyuan-Pano-DiT, providing global context.\n2. Feed-forward Panorama 3D Gaussian Splatting (3DGS) network, converting the panorama into a 3D geometric scaffold by reformulating panoramic depth estimation as a multi-view stereo matching problem.\n3. Scaffold-guided novel view synthesis, generating photorealistic novel views conditioned on both the 3D scaffold and anchor views through a Dual-LoRA training strategy and 3D attention fusion.\nThe method yields significant improvements in photorealism, geometric accuracy, and stability under large viewpoint changes, outperforming DreamScene360, WonderJourney, SEVA, and VMem across multiple metrics.", "questions": "1. Geometric consistency validation:\nAlthough quantitative results are comprehensive, the paper lacks explicit geometric visualizations such as reconstructed point clouds or extracted meshes. Given that geometric consistency is a central claim, could the authors provide additional evidence, to directly demonstrate 3D structural accuracy beyond continuous frame visualizations?\n2. Generalization to dynamic and outdoor environments:\nHow does the proposed framework perform on more complex, dynamic real-world scenarios, such as urban or outdoor scenes containing moving objects (e.g., pedestrians, vehicles)? Have the authors tested or considered extensions for handling dynamic content or temporally varying geometry?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The overall pipeline is well-structured and intuitive, decomposing the single-image 3D generation problem into panorama expansion, geometric scaffold reconstruction, and scaffold-guided view synthesis. Each stage has clear motivation and contributes coherently to the final performance. And the paper is easy to follow with clear logistics.\n2. The authors conducted comprehensive experiments with both quantitative and qualitative results.The sufficient evidence through extensive metrics makes the results solid.\n3. The ablation studies are clear, for core components (Dual-LoRA Training, Memory Condition,  Bidirectional Fusion Module), there are sufficient experiments to show the effectiveness of them.\n4. High efficiency and scalability. The proposed method achieves fast 3D reconstruction (0.5 s on H20, 0.1 s on H100) while maintaining visual quality, showing strong potential for real-world applications that require interactive generation like robot policy learning.", "weaknesses": "1. Although quantitative results are sufficient, the paper lacks explicit visualization of geometric outputs such as reconstructed point clouds or extracted scene meshes. There are many visualization with continuous frames in the website, however, geometric consistency is one of the paper’s core claims, these visualizations would provide more direct and convincing evidence of 3D structural accuracy.\n2. The method has not been tested on complex, dynamic real-world environments, such as urban or outdoor settings with moving objects like pedestrians, vehicles. It remains uncertain how well the framework generalizes beyond indoor or static scenarios.\n3. The pipeline depends on several large pretrained models (e.g., Hunyuan-Pano-DiT, SEVA, VGGT), which limits the degree of innovation and increases the engineering nature of the work. However, this design choice is understandable and acceptable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Z2HhzwMCUR", "reviewer": "Reviewer_U23k", "summary": "This paper explores how to leverage a 3D representation to condition the generation of new viewpoints given a single viewpoint. They propose a pipeline that takes an image, uses an off the shelf model to predict panoramas, then selects anchor views and from this constructs a 3D representation using a trained neuraal network. This is then rendered to the target view and, with the anchoring view, fed as a prior to the diffusion model.\n\nThe paper's point is that explicitly having an accurate 3D representation is key to obtaining good, robust, generated views as extreme viewpoints and they aim to demonstrate that their approach of doing so is SOTA.", "questions": "1. For Figure 1, it's quite confusing what I'm looking at -- what was input to the model, what was it tasked to do and what was the output? This is useful to put the results in context.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper appears to have done comprehensive ablations:\n1. Comparing sub parts of their approach (the 3D generation) and the image generation part (by using others' 3D representation), finding improvements in both cases.\n\n2. The use of a fixed 3D representation makes a lot of sense to fix geometric mistakes / ambiguity. By creating an approximation of the whole 3D scene a priori, the authors get around issues taht exist in other works due to the accumulation of errors.", "weaknesses": "1. Why no comparison with Cat3D ? This seems like an obvious baseline which also does 1 view to multiple views ? The paper focusses on other methods that, to my understanding, seem to be mroe aimed to handle a trajectory of views (e.g. SEVA / AnySplat) and so may in general be expected to handle more complex scenes. While the author's approach is better than these in this case, they should also compare it to methods with more similar aims (e.g. Cat3D).\n\n2. How does the model fair if you don't do a loop closure setting -- e.g. where you keep moving 'forward' by zooming in on a location? I fear in this case the panorama wouldn't be so useful for maintaining consistency and so is this method really only better than others in this one case for the camera angles ?\n\n3. The authors evaluate their full pipeline on only one dataset -- but surely they could explore others as well ? Such as those in Table 1 in the Cat3D paper ?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8WOXaDF2P4", "reviewer": "Reviewer_7LcG", "summary": "The manuscript proposes a framework for generating an explorable 3DGS scene from a single perspective image. For doing that, authors decompose the problems in three independent tasks: 1. they generate a panorama 2. they select 2D anchor views from this panorama and create a 3DGS based scaffold by solving a multi-view stereo matching problem; 3. they use the scaffold for creating novel views at arbitrary camera poses.  They assess the framework in multiple ways: panoramic depth estimation, scaffold reconstruction, and explorable generation. The project website contain additional information and qualitative results.", "questions": "1. How the method works on the wild, with casual images? What are the limitations for usage in a game or VR scenarios? Image resolution, details in extreme zooming, etc. I would expect an extensive assessment in supplementary, and some information on the main manuscript\n2. How is the method related to competing baseline, like SAVA+?  How is the related to Pano2Room? I would expect a detailed discussion on how the pipeline differs from this paper, as well as a benchmarked comparison against it.\n3. Can you provide additional details about the bidirectional fuse model, since it it a key component in the proposed architecture? Some details about Cube Projection can be shortened, instead.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well written and technically solid. Narrative style is convincing, and the manuscript is easy to follow.\n2. The problem is really challenging and worth of exploration: 3D content generation from single images and automatic world generation has a strong impact on media creation.\n3. The proposed solution appears convincing and well designed; technical details are provided, and authors indicate they will release the code for replicability.\n4. The website resource contain a good variety of additional qualitative results: they look impressive and they show that the proposed framework has a strong potential.\n5. Assessment: the framework is benchmarked against the most modern SOTA, and the relevant literature is generally discussed and compared. Quantitative and qualitative results are generally convincing.", "weaknesses": "1. Practical usage: even if the qualitative results appear impressive, I would expect attempts to go at different level of detail. I would like to see some examples of animations with different level of zoom and showing cluttered areas. For example, I would expect attempts to generate detailed leaves in plants, etc..  In most of cases, the generated scenes are with limited clutter. Also, I would expect to see how it works on the wild, with some casual image. Also, it is importante to clearly state what are the image resolution limitations.\n2. Comparison with SOTA: in related work section, I would expect some sentences showcasing in detail how the proposed framework improve current technologies.\n3. Link to panoramic scene reasoning: since the proposed framework pass through a panorama generation, and then it does all the subsequent tasks on a panoramic image with depth estimation; I would expect a comparison with methods generating 3DGS or explorable scenes from single panoramic images, like for example Pano2Room \nPu, G., Zhao, Y., & Lian, Z. (2024, December). Pano2room: Novel view synthesis from a single indoor panorama. In SIGGRAPH Asia 2024 Conference Papers (pp. 1-11).\nhttps://github.com/TrickyGo/Pano2Room.  Actually, the strategy presented is quite similar to Pano2Room, and I would expect at minimum a comparative discussion, and probably also a benchmark against this baseline.\n4. Minor: for fairness, in qualitative results authors should use the same camera poses for baselines. \nline 57:  topicm\nlines 199-201: please rephrase it. Lots of methods try to perform depth estimation from single panorama for novel view synthesis, like\nPintore, G., Bettio, F., Agus, M., & Gobbetti, E. (2023). Deep scene synthesis of Atlanta-world interiors from a single omnidirectional image. IEEE Transactions on Visualization and Computer Graphics, 29(11), 4708-4718.\nFigure A7: one of them is SEVA+, which one?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)