The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and world modeling. While panoramic images have proven effective for scene initialization, existing approaches suffer from a trade-off between visual fidelity and explorability: autoregressive expansion suffers from context drift, while panoramic video generation is limited to low resolution. We present Stepper, a unified framework for text-driven immersive 3D scene synthesis that circumvents these limitations via stepwise panoramic scene expansion. Stepper leverages a novel multi-view 360° diffusion model that enables consistent, high-resolution expansion, coupled with a geometry reconstruction pipeline that enforces geometric coherence. Trained on a new large-scale, multi-view panorama dataset, Stepper achieves state-of-the-art fidelity and structural consistency, outperforming prior approaches, thereby setting a new standard for immersive scene generation.
Figures & tables
Figure 2: Method overview. a) Our model generates a new panoramic image from a previously unobserved viewpoint based on a given input panorama. To ensure high quality, we utilize a pre-trained diffusion model with expanded multi-view attention that is instrumental for jointly denoising the high-resolution cubefaces of the newly generated novel-view panorama. b) Our ability to generate novel-view panoramas enables auto-regressive scene generation in all directions of the scene yielding a set of high quality, consistent panoramas that effectively complete the representation of the 3D scene. c) The generated panoramas are processed with the feed-forward reconstruction model, i.e. MapAnything. The output pointcloud serves as the initialization of a custom 3D Gaussian Splatting reconstruction enabling high quality novel view synthesis of the generated 3D scene.
Figure 3: Dataset Samples. The dataset generated with Infinigen consists of a diverse set of high quality synthetic panoramas of indoor and outdoor scenes. For every panorama, we rendered a pair from a novel viewpoint enabling the training of the multi-view panorama generation model. All panoramas are aligned to the horizontal line.
Figure 4: 3D Scene Generation. We provide visual example of generated novel-view panoramas on the left side. The details of the initial panorama are well preserved and previously unseen regions are filled in. On the right side we show novel-view renderings of the reconstructed scenes indicating the 3D consistency of the generated panoramas.
Figure 5: Comparison with Baselines. Given a high quality input panorama, we observe that our approach achieves consistent scene generation while showing significantly more details and sharpness in the rendered novel view images in comparison to the baselines.
Infinigen Indoors
PSNR ↑
SSIM ↑
LPIPS ↓
WorldExplorer
11.864
0.674
0.739
LayerPano3D
18.305
0.783
0.509
Matrix-3D
18.532
0.753
0.502
Ours
21.775
0.797
0.430
Infinigen Outdoors
PSNR ↑
SSIM ↑
LPIPS ↓
WorldExplorer
13.912
0.561
0.594
Table 1: Quantitative Evaluation. We compare our approach to the baseline methods for three different datasets on common image metrics. We observe that our approach yields significant improvements on all metrics and datasets.
Figure 6: Single vs multiple panoramas to 3DGS. The multi panorama input to the 3DGS reconstruction consistently fills in the unobserved regions in the initial panorama without sacrificing the quality of the input panorama.
Figure 7: Effect of step size. Novel panos. generated by a model with a) adjustable step direction, b) a larger step size d=0.5m .
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Table 2: User study results. n=10 participants cast votes on the most visually appealing , most accurate geometry and most details categories for 15 video comparisons, yielding 150 votes per category in total.
Our stages (single step = 30s)
Ours vs Baselines
1. Gen. n=8 novel panos
n× 30s
WorldExplorer
7h
2. MapAnything
20s
Matrix-3D
40min
3. Filtering
2min
LayerPano3D
32min
4. 3DGS optimization
8min
Ours
15min
Appendix
Table 3: Inference time - Panorama to 3D scene.
Figure 8: Impact of Multi-View Panoramas We depict the effect of using various options of panorama input for MapAnything and found that the auto-regressive expansion and sampling yield the most complete and accurate outputs.
Figure 9: Auto-regressive steps. We perform a number of auto-regressive steps to generate novel-view panoramas from the initial panorama to the left and right for several examples. From the generated panoramas, we visualize the forward-facing view to highlight the generated details. A single step corresponds to a step length of 0.25m .
Figure 10: Multi-view panoramas pairs generated with Infinigen.
Figure 11: Input panoramas used for testing.
Figure 12: User study form. Participants fill out this randomized form and cast a single vote per category.
We present GuidedSceneGen, a text-to-3D generation framework that produces metrically accurate, globally consistent, and semantically interpretable indoor scenes. Unlike prior text-driven methods that often suffer from geometric drift or scale ambiguity, our approach maintains an absolute world coordinate frame throughout the entire generation process. Starting from a textual scene description, we predict a global 3D layout encoding both semantic and geometric structure, which serves as a guiding proxy for downstream stages. A semantics- and depth-conditioned panoramic diffusion model then synthesizes 360° imagery aligned with the global layout, substantially improving spatial coherence. To explore unobserved regions, we employ a video diffusion model guided by optimized camera trajectories that balances coverage and collision avoidance, achieving up to 10x faster sampling compared to exhaustive path exploration. The generated views are fused using 3D Gaussian Splatting, yielding a consistent and fully navigable 3D scene in absolute scale. GuidedSceneGen enables accurate transfer of object poses and semantic labels from layout to reconstruction, and supports progressive scene expansion without re-alignment. Quantitative results and a user study demonstrate greater 3D consistency and layout plausibility compared to recent panoramic text-to-3D baselines.
Stefan Ainetter, Thomas Deixelberger, Edoardo A. Dominici +3
Huawei Technologies, Austria · Graz University of Technology, Austria · Huawei Technologies, Switzerland
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: https://one2scene5406.github.io/
World building with 3D scene representations is increasingly important for content creation, simulation, and interactive experiences, yet real workflows are inherently iterative: creators repeatedly extend existing scenes under user control. Motivated by this gap, we study text-guided 3D scene expansion via free-form view insertion. Starting from a real scene captured by multi-view images, a user specifies a text expansion intent, which a generative model materializes as an inserted view extending the scene coverage. Unlike simple object editing or style transfer within a fixed scene, the inserted view may be 3D-misaligned with the original reconstruction, introducing geometric shifts, hallucinated content, or view-dependent artifacts that disrupt global multi-view consistency. To address this challenge, we propose SceneExpander, which applies test-time adaptation to a parametric feed-forward 3D reconstruction model with two complementary distillation signals: anchor distillation stabilizes the captured scene using geometric cues from the captured views, while inserted-view self-distillation retains insertion-supported predictions to accommodate the misaligned view. Experiments on ETH scenes and online data demonstrate improved expansion behavior and reconstruction quality under misalignment.
Zijian He, Renjie Liu, Yihao Wang +5
Sun Yat-sen University · KlingAI Research, Kuaishou Technology