SatDreamer360: Multiview-Consistent Generation of Ground-Level Scenes from Satellite Imagery
Authors: Xianghui Ze
Beiyi Zhu
Zhenbo Song
Jianfeng Lu
Yujiao Shi
Venue: ICLR 2026 Poster (2026)
Keywords: Satellite-to-Ground View Synthesis
Cross-View Image Translation
Diffusion-based Scene Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Generating multiview-consistent $360^\circ$ ground-level scenes from satellite imagery is a challenging task with broad applications in simulation, autonomous navigation, and digital twin cities. Existing approaches primarily focus on synthesizing individual ground-view panoramas, often relying on auxiliary inputs like height maps or handcrafted projections, and struggle to produce multiview consistent sequences. In this paper, we propose SatDreamer360, a framework that generates geometrically consistent multi-view ground-level panoramas from a single satellite image, given a predefined pose trajectory. To address the large viewpoint discrepancy between ground and satellite images, we adopt a triplane representation to encode scene features and design a ray-based pixel attention mechanism that retrieves view-specific features from the triplane. To maintain multi-frame consistency, we introduce a panoramic epipolar-constrained attention module that aligns features across frames based on known relative poses. 
To support the evaluation, we introduce VIGOR++, a large-scale dataset for generating multi-view ground panoramas from a satellite image, by augmenting the original VIGOR dataset with more ground-view images and their pose annotations. Experiments show that SatDreamer360 outperforms existing methods in both satellite-to-ground alignment and multiview consistency.

Published as a conference paper at ICLR 2026
SATDREAMER360: MULTIVIEW-CONSISTEN GENER- ATION OF GROUND-LEVEL SCENES FROM SATELLITE IMAGERY
Xianghui Ze', Beiyi Zhu?, Zhenbo Song', Jianfeng Lu', Yujiao Shi Naajiing University Science and Technology. "ShanghaiTech University {zexh, songzb, lujff just. edu. cn, /zhuby2025,
edu.
ABSTRACT
Generating multiview-consisten 360 ground-level scenes from satellite imagery is challenging task with broad applications in simulation, autonomous navigation, and digital twin cities. Existing approaches primarily focus on synthesizing individ- ual ground-view panoramas, often relying auxiliary inputs like height maps handcrafted projections, and struggle to produce multiview consistent sequences. this paper, we propose SatDreamer360, framework that generates geometrically consistent multi-view ground-level panoramas from a single satellite image, given predefined pose trajectory. To address the large viewpoint discrepancy between ground and satellite images, we adopt a triplane representation to encode scene fea- tures and design ray-based pixel attention mechanism that retrieves view-specific features from the triplane. To maintain multi-frame consistency, we introduce a panoramic ciiostar-oostr attention module that aligns features across frames based on known relative poses. To support the evaluation, we introduce VIGOR++, large-scale dataset for generating multi-view ground panoramas from satellite image, by augmenting the original VIGOR dataset with more ground-view images and their pose annotations. Experiments show that SatDreamer360 outperforms existing methods in both satellite-to-groun alignment and multiview consistency.
INTRODUCTION
Generating ground-level scenes from satellite imagery has attracted significant attention due to the broad coverage and low acquisition cost of satellite images. This task shows promising applications in autonomous driving (Villalonga Pineda (202)): Lu et al. (2024)). 3D reconstruction (Liu et al. (2024);
Yan et al. )2244) and data augmentation (Yng et al. (2023) 3 Gao et al. (2023) for downstream tasks.
Many existing works (Li et al. (2024a): Lin et al. (2024), x Xu & Qin (024)) Ze et al. (2025) focus on generating individual ground images from satellite views, leaving the continuity of multi-ground views largely unaddressed. In this paper, we aim to synthesize multiple ground-view images from single satellite image, controlled by a predefined trajectory. This introduces new challenges in maintaining both geometric consistency with the top-down satellite image and multiview coherence across the sequence of generated frames.
Early approaches (Isola et al. (2017a); Regmi & Borji (2018) Shi et al. (2022) y Lu et al. (2020); Qian
al. (2023)) formulate cross-view synthesis as a one-to-one mapping problem, often implemented with Conditional Generative Adversarial Networks (cGANs). These methods focus on aligning representations at pixel or perceptual level. However, the extreme viewpoint disparity between top-down satellite views and strect-level images leads to limited field-of- overlap. Satellite images inherently miss key elements such as building facades, tree trunks, and other occluded details, making the ground view generation task highly mnder-constrained and naturally one-to- one-to-many.
Recent advances leverage latent diffusion models (LDMs) (Rombach et al. (2022)) to better handle this uncertainty (Li et al. (2024a) Lin et al. 2024 ; Deng al. (2024); Xu & Qin (2024); Ze et al.
2025 These methods introduce prohabilistic modeling to produce diverse and high-fidelity ground
"Corresponding author

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Published as a conference paper at ICLR 2026
ControlS.5 St
GT
Satellite image && Trajectory
SalDnommr1600
Contool-1 200 GI
Given satellite image and sequence of query poses (colored stars), goal is to synthesize coherent panoramic views along the trajectory. The proposed SatDreamer360 generates more realistic and geometrically consistent ground-level scenes compared to state-of-the-art methods, faithfully capturing spatial layouts and structural continuity across diverse environments.
Figure 1:
images. However, they often rely approximate projections (Lin et al. (2024); Ze et al. 2025) or auxiliary data such as height maps (Li et al. (2024a) Deng et al. (2024); Xu & Qin (2024)) which can be difficult to obtain at scale. Moreover, while effective for single view generation, these models fall short in producing multiview consistent sequences, which are critical for applications like simulation, planning, digital twin city modeling. A recent effort (Xu & Qin (2025) attempts to generate continuous ground-view videos by leveraging multi-angle satellite imagery in a two-stage pipeline: the first stage generates base frame, followed by autoregressive generation of future frames. While this improves continuity, the reliance on multi-view satellite input and complex coordination for different generation stages reduces practical applicability.
In this paper, we present SatDreamer360, a unified framework that generates continuous and coherent ground-view sequences from a single satellite image and target trajectory. as shown in Figure [ The key idea is to embed explicit cross-view geometric reasoning between satellite and ground views, as well as across ground frames into the latent diffusion process.
We adopt a compact triplane representation (Huang et al. (2023) Bhattarai et al. (004)) Shue et al. (2023) encode scene geometry directly from the satellite image, avoiding the need of height maps (Deng et al. (2024); Xu & Qin (2025)) or handcrafted projections (Ze et al. (2025)). We further design a ray-based pixel attention mechanism that retrieves view-dependent features from the triplane and integrates them into conditional diffusion, enabling geometry- aware and controllable generation.
To enhance mutiview consistency, we draw inspiration from the use of epipolar constraints in pinhole cameras (Tobin et al. (2019); He et al. (2020); Huang et al. (2022) 2024)) and extend the idea to panoramic images with equirectangular projections. We design an epipolar-constranee attention module for panoramic images, which aligns features across frames by leveraging the known relative camera poses.
Finally, to support lcage evaluation, construct VIGOR++, extension of the VIGOR dataset with ground-truth trajectories and continuous ground-view sequences, providing a new benchmark for cross-view generation. To summarize, our contributions are follows:
A unified framework, SatDreamer360, for generating continuous and geometrically consis-
tent ground-view sequences from a single satellite image and a target trajectory.
A ray-guided cross-view feature condition mechanism that encodes the 3D scene with triplane representation and aggregates spatial cues pixel by pixel via ray-based attention, enabling geometry-aww and controllable diffusion-based ground-view generation.
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An interframe attention module that uses panoramic epipolar constraints via equirectangular projections to align features across frames, enhancing multiview consistency. A new VIGOR+ dataset, which extends VIGOR with continuous sequences and trajectory annotations, providing benchmark for cross-view sequence synthesis.
RELATED WORK
Cross-view ground scene generation aims to reconstruct ground scenes from other perspectives,
such aerial (Xu et al. (2023); Gao al. (2024) landmark images (Yang al. (2023): Li al.
(2024b): Gao et al. (203)); ); Swerdlow et al. (2024).. Given the wide availability of satellite imagery, related research (Shi al. (2022); Qian et al. (2023); ; Xu & Qin (2024); Ze et (2025); Li al.
(2021 2024d): Xu & Qin (025)) focuses on satellite-to-rrond generation. Previous works (soola ct al. (017a): Regmi Borji (2018) implicitly convert satellite image features into ground map representations, often causing geometric distortions. Later methods introduced approximate projec- tions (Lu et al. (2020); Shi al. (2022); Lin et al. (2024); Ze et al. (2025)). height maps (Marí et al. (2022): [u et al. (2020)) Deng al. (2024); Xu & Qin (2024); Li et al. (2224a) Xu & Qin )) or estimating density maps (Qian et al. 2023)) as priors. However, their accuracy is constrained by errors in the estimation priors, and ground-image-bae image-baset methods often over-rely these inaccurate projections while neglecting the broader contextual information available from satellite imagery.
Multiview consistent image generation aims to generate multiview continuous frames from given prompts. Early Gan-based approach (Vondrick et al. (2016); Rematas et al. 2022 has been surpassed by methods utilizing the diffusion architecture (Blattmann et al. (2023b Diffusion Models (VDM) (Blattmann et al. (2023b;; ; Singer et (2022); Wu et al. (2023) introduce spatiotemporal modules into U-Net generate coherent sequences, though with high computational cost. Some methods designed for single-object multi-view generation, such as MV MVDream (Shi et al. (2023b) and dilfuusion process. However, when applied to large-scale scene generation. such implicit conditioning insufficient to enforce strict pose constraints. Recent works (Tseng al. (2023) Huang al. (2024) incorporate epipolar constraints to enforce multiview consistency, but are mostly limited to pinhole cameras, with little exploration of panoramic settings. More broadly, methods such as (Liu et al. (2023); ; Kong et al.] )2024); Voleti et al. (2024); [Bourigault & Bourigault (2024)) generate multi-viww images from single-object inputs, yet they focus on object-level generation and cannot address scene-level continuity.
a)), where Video
(Shi al. (2023a)), implicitly encode camera embeddings directly into the
3 METHOD
Given satellite image S and set of F-DoF ground camera poses {p' = [t', | where 1 denotes spatial location and vi' the yaw angle, our goal is to synthesize a sequence of ground panoramic images G' that are spatially aligned with the satellite view and consistent across multiple views.
To obtain the optimal solution for ground image inference, as illustrated in Figure 2 we develop SatDreamer360 based latent diffusion model (Song al. (2020); Blattmann et al.) (2023b)) to synthesize ground-level views conditioned on the satellite image and camera pose. It generates ground images by iteratively denoising a random Gaussian noise for T steps, learning predict the Gaussian noise injected at each step l:
L Ezn.c,e, ||| co(Vārz0 vI- t,),
Here, ep is the denoising network using U-Net, is the conditioning comp--sinn the satellite image S and pose p". āe is the variance schedule, and is drawn from standard Gaussian distribution. Ground images are encoded using a VQ-VAE (Esser et al. (2021) encoder {G) to obtain latent codes For clarity, refer ground representations in latent space also G in what follows.
3.1 RAY-GUIDED CROSS-VIEW FEATURE CONDITIONING
Spatial Representation via Triplanes. To represent the 3D scene covered by the satellite image, adopt tri-plane structure (Chan et al. (2022) Huang et al. (003)). ). lightweight and expressive
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3 Ruy-Gd Cross-View View Feature Conditioning
Egepola-Coosttaaieee Ameriion
R.
CVHA
Ground Panoramas
Satellite image
Ray- Based Pixel Attention
Epipolur-Constraint
Ns.
Stu.
p
Epipolar-Cone Constraint
S.c.3.2 Chipola-CConstrainet Attention
Figure 2: Overview of the proposed SatDreamer360 framework. Given a single satellite image and a target trajectory, our model synthesizes continuous ground-level panoramas along the path. A Ray-Based Pixel Attention mechanism retrieves view-specific features through cross-view geometric reasoning, guided by a tri-plane representation of the scene. An Chipolar-Constrainee Attention module aligns features across frames using relative camera poses.
alternative, instead of the information-spane BEV representation (Li et al. (024c)) or the computa- tionally intensive voxel representation (Li al. (2022)). Three orthogonal planes XXY are defined in the tri-plane representation, with the XY plane parallel to the ground.
Given point in 3D space, its feature Fry= obtained by aggregating the features from its projections onto the three planes:
Fays = Fay + Faz + Fyes
where Fzy, Faz, and Fyz denote the interpolated features from the corresponding 2D planes, and + denotes their element- wise summation to obtain the final 3D feature.
To construct the triplane representation, we initialize the planes by extracting features from the satellite image using ResNet (He et al. (2016)), which naturally aligns with the top-down XY plane. To enrich spatial reasoning across all three orthogonal planes, we apply Cross-view Hybrid Attention (CVHA Li al. (2024c)), enabling interactions among the XY, X Z, and Y planes. Each plane aggregates projections from the other two, enriching its features with complementary spatial context. For instance, the updated features on the XY plane are computed as:
ptop CVHA (1aop Ref2g)
Refap Eoop U (F U [Ffreor).
Here, Fry denotes the point feature on the XY plane. The reference set Ref contains local neighbors sampled along the Z-axis from the orthogonal X and Y Z planes, denoted as (Ffront) and { (eide)) This cross-plane aggregation enables each point on the triplane to incorporate multi- view cues, thereby enhancing 3D spatial consistency. Moreover, in sequential settings, previously synthesized ground-view images can be projected back and integrated into the triplane to refine its rep- resentation. With CVHA, this incremental update yields a more expressive and temporally coherent scene model. Further architectural and implementation details are provided in the Appendix A.5
Ray-Based Pixel Attention. Conventional cross-attention mechanisms (Rombach et al. (2022) typically align global prompts with image-level semantics but often fail to respect underlying 3D scene geometry. This limits their ability to establish accurate cross-view correspondences, particularly in view synthesis tasks. To address this, propose Ray-Based Pixel Attention module that incorporates geometric priors by explicitly conditioning attention on camera rays.
Specifically, illustrated in Figure (top middle) and Appendix A.2 each pixel gu.v location (u, v) in the panoramic ground-view image G R XX corresponds to unique 3D ray Ru,,.
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Groond View
Figure 3: Overview of the VIGOR+ dataset. (a) The map of Seattle, USA, serves as an example of the ten cities in the dataset. The red boxes and blue boxes represent the districts for the training set and test set, respectively. (b) shows a road map. Dots and stars along the road represent locations of ground images and satellite images. Two of them, marked with the red star and green star, are shown in (c). (d) shows the continuous ground sequence within one satellite image.
parameterized by yaw va and pitch 9 angles:
H //W x 2T, On 0 - x n
uu.0
These angular parameters define the direction of the ray Ru, in the camera coordinate system. The ray originates from the ground-view camera position, and its direction is uniquely defined by (Nu,e, 0,,w). To encode spatial cues along each ray, we sample K points at evenly spaced depths 1K and project them into the spatial coordinate system using the camera pose, yielding 3D positions Xu,,k Features at these 3D positions then extracted from the tri-plane representation using deformable attention:
J W, > Ang + Am
K
F ((n.v)
Σ
(5)
where is the number of attention heads, W, is learnable weight for head j. AXk,j is an offset along the ray around the sampled points, initialized zero, and Ak.j denotes the attention weight for these sampled points, normalized such that Σ AR.5 for each head. Both offsets and attention weights are dynamically refined across iterations, guided by the evolving ground latent feature map. F(x..... Axx.;) denotes features extracted from the triplane at the adjusted 3D positions using Eq. The aggregated feature F 9g...) at pixel (u, v) guides the U-Net in integrating satellite information, effectively aggregating spatial cues pixel by pixel.
3.2
ATTENTION
To maintain mutiview consistency across consecutive frames in a lightweight and efficient manner, we draw inspiration from apipolar-constrainee attention in pinhole images (Tseng al. (2023) and extend it to outdoor panoramic imagery. Specifically, introduce attention mechanism tailored for equirectangular projections. For two frames ground panoramas, Gm and G". a pixel " on Gm corresponds to set of candidate pixels on G", enforced the underlying geometric constraint (proof Appendix A.3):
(P- (ot, Imn Rmn (P-) O.
(6)
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Here, the point set (g m/) on G" denotes candidate matches that satisfy the constraint relationships. The terms Rmn and Emn denote the relative rotation and translation between frames m and n, and Emn is the skew-symmetrii matrix of tms. The mapping P is the equirectangular camera projection defined in Eq. Therefore, when establishing mutiview consistency, we do not need to perform pixel-wise correspondence for the entire image as in previous work (Wu et al. (2023); Xu & Qin (2025)). Instead, we restrict attention to points that satisfy the epipolar constraint, significantly reducing redundancy while preserving geometric fidelity:
QK
F ag softmax
V.Q W
W Po W F10
(7)
Vd
where W WK, and W are learnable matrices for query, key, and value. This epipolar-constrainge attention applied at multiple U-Net levels to fuse coarse and fine-grained features.
By restricting attention to points that satisfy the epipolar constraint, the computational complexity reduced from ONN HW x NHW) to ((N HW NM,, where N the number of frames in sequences, H and W denote the height and width of the feature map. and M is the number of sampled points satisfying epipolar constraints with M < H.W To further improve efficiency, we adopt a sparse querying strategy that uses only reference frames: the very first frame of the sequence and the immediately preceding frame. The first frame acts as global anchor, ensuring consistency in overarching scene characteristics such weather and illumination. Simultaneously, the constraint with the previous frame preserves local geometric coherence.
3.3 VIGOR++: EXTENDING VIGOR FOR SATELLITE-TO-GE TR-GUNND VIDEO GENERATION
Existing cross-view datasets lack continuous panoramic sequences. To address this, construct VIGOR++, an extension of the VIGOR dataset (Zhu et al. (2021)) tailored for large-scale, consistent cross-view generation, enabling the dataset be more widely used in 3D scene reconstruction, cross-view video localization tasks, as shown in Figure 3 To broaden the coverage of satellite maps for the task of large-scuus scale scene generation. we expand the wide-area satellite map dataset by increasing it from the original 70 m x 70 m to 160 m x 160 m from Google Maps (goo (a). Subsequently, we include additional cities. Apart from the initial cities of Chicago, New York, San Francisco, and Seattle, we integrate datasets for six additional regions: Atlanta, Bismarck, Kansas, Nashville, Orlando, and Phoenix. This augmentation enriches the variety of urban representations within the dataset.
To obtain continuous ground sequences, we extract all available Google Street View (goo (b) images within the satellite region. Subsequently, we employed a semi-automatic approach to organize sampling paths for each satellite image. By leveraging sky color histograms and image embedding similarities, constructed a connectivity graph and executed path extraction based depth-first. search to identify potential routes. Subsequent manual refinement ensured multiview coherence.
Our efforts yielded more than 90,000 novel view satellite and ground video pairs. As shown in Figure [i these images are evenly distributed across ten cities, covering a total area of 117.47 km?. Most trajectories contain between 7 and 16 ground-view frames. The average frame interval is approximately 11 m, with the minimum interval being 0.079 m and the maximum reaching 20 m. Of these, 84,055 pairs are designated for training, while 7.443 are allocated for testing. To evaluate the model's 's generalization capabilities, the testing set is collected from locations entirely distinct from the training data.
EXPERIMENTS
Experimental Setup. We use 256 x 256 satellite images and the 4-DoF camera poses of ground-view images as input, aiming to generate continuous ground-view sequences for fair comparison with prior work. Our model is finetuned based on the pre-trained Stable Diffusion 1.5 model (Rombach et al. (2022)). In our experiments, we set the default number of ray samples to K= 8, and the number of points sampled along the epipolar lines to M 4. Detailed analysis is provided in Appendix A.14 We first perform 300 epochs of finetuning on a single-image generation task, followed by additional 300 epochs on continuous sequence data dataset to learn temporal consistency. During inference, we adopt DDIM sampling with 50 steps for efficient generation. All experiments are conducted using four NVIDIA L.40 GPUs.
resolution of 128 x 512

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EschurNet
ControlSSS
Otuss
GT
EscherNet ComiroS22
Ours
Figure 4: Qualitative comparison of ground-level image sequences along trajectories T1 and T2, shown from left to right. The corresponding satellite images and trajectories are provided in Figure 13 Our method produces more realistic textures and preserves structural and spatial continuity across frames, demonstrating stronger multiview coherence and environmental fidelity across diverse scenes.
Table Quantitative comparison with existing algorithms VIGOR+ dataset.
Perceptual level
Semantic level
Pixel level
Multiview level
Method
DDepth
Paaee
LFID
JDINO
JSegAny
1SSIM
1PSNR
LFVD
JCLIPSIM
Sa12Den
0.4584
133.6
4.437
0.3729
0,3892
12.06
11.70
8.405
7.671
EscherNet
0.5581
84.21
4.942
0.3845
0,2587
11.23
7.282
8.250
10.50
ControlS2S
0.4433
29.48
4.567
0.3753
0,3718
11.84
4.871
10.81
6.651
Ours
0.3955
27.41
4.156
0.3563
0.3964
12.75
2.101
6.820
5.623
Datasets. For the single ground-view image generation task, we use the CVUSA (Zhai et al. (011
dataset, which primarily focuses rural areas, and VIGOR (Zhu et al. (2021) Lentsch al.
(0022
dataset, which covers four major cities, following the same protocol as prior works (Shi et al. (2022)
Qian al. ) Ze et al. (2025)). These cross-view datasets provide one-to-one correspondences
between panoramic ground images and satellite images. CVUSA contains 35,532 pairs for training and 8,884 pairs for testing, with most scenes focusing on rural areas. VIGOR comprises data collected from four cities-Nee York, Seattle, San Francisco, and Chicago pairs for training and 52,605 pairs for testing. For the continuous scene generation task, conduct experiments using our proposed VIGOR+ dataset, where the training and testing sets are collected from entirely distinct regions.
Chicago-resullting resultinn in 52,609
We evaluate the authenticity and multiview consistency of generated images. For authenticity, we compare results with ground truth (GT) using pixel-level metrics (SSIM, PSNR, SD) and perceptual metrics based on pretrained networks (Palex (Krizhevsky et al. (2012)), P Paqueeze (Tandola ct al. (2016)), and FID (Heusel al. 2017 Since real-world variations weather and season can cause color shifts, strict pixel-level comparisons may be less informative. Following Ze et al.
Evaluation Metrics.
(2025), we emphasize structural and semantic similarity. We employ DINO (Caron et al. (2021) and Segment Anything Kirillov al. (2023) to extract semantic features, and use DepthAnything Yang jet al. (2024)) for depth consistency. In addition, we employ LRCE (Shen et al. (2022) to measure the continuity of panoramic images along the left and right boundaries.
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(a) Sat
(b) Sat2Den
(c) ControlS2S
(d) Ours
(c) GT
Figure 5: Qualitative comparison with previous works on satellite to single ground image generation, our model can effectively capture roadways, ground markings, and architectural details.
Table 2: Quantitative comparison with previous works on satellite to single ground image generation.
Perceptual Level
Semantic Level IDINO
Pixel Level PPSNR
Method
[Depth
JLRCE
Pequeeze
Paier
LFID
J.SegAny
SSSIM
SSD
Pix2Pix
0,3468
0,5084
44.51
5.242
0.3847
0.3190
13.20
12.08
21.85
18.81
CVUSA
$2S
0.3218
0,4830
29.49
5.112
0,3852
0.3508
13.40
12.30
21.05
19.10
Sar2Density
0.3217
0.4634
47.85
4.945
0.3763
0.3307
13.46
12.27
19.83
16.17
CrossDiff
23.67
0.3710
12.00
ControlS2S
0.3192
0.4323
21.30
4.807
0.3612
0.3753
13.67
12.33
19.58
14.62
Ours
0,3146
0.4255
17.00
4.807
0.3602
0,3812
13.88
12.42
19.36
14.53
Pix2Pix
0.3346
0.4513
67.96
4.717
0.3833
0.3714
13.33
12.93
8.647
6.569
$2S
0,3694
0.4941
121.1
5.032
0.4037
0.3273
12.16
12.31
10.87
9.790
VIGOR
Sat2Density
0.2828
0.3898
54.49
4.408
0.3627
0.3956
14.14
12.38
8.054
5,805
ControlNet
0.3395
0.4594
23.68
4.950
0.3916
0.3397
12.02
12.59
10.02
7.499
ControlS2S
0.2729
0.3770
28.01
4.335
0.3529
0.4228
13.80
13.07
7.095
5.176
Ours
0.2598
0,3469
21.36
4.287
0.3471
0.4385
14.08
13.11
6.727
5.081
Multiview consistency across frames evaluated using Fréchet Video Distance (FVD) (Unterthiner et al. (2018) and CLIP-based similarity (CLIPSIM) (Wu et al. (202T)), two common metrics for assessing temporal coherence and perceptual alignment in generated video sequences. FVD quantifies the distributional similarity between generated and real videos by computing the Fréchet distance between feature representations extracted from a pretrained video recognition network, capturing both spatial appearance and temporal dynamics. In addition, CLIPSIM measures semantic and perceptual similarity using representations from the CLIP model, which encodes visual content in joint vision-language embedding space. By comparing these embeddings, CLIPSIM estimates how well semantic information is preserved across frames or between generated and reference videos.
4.1 COMPARISON WITH PRIOR WORK ON SATELLITE-TO-GROUU SEQUENCE GENERATION
Generating continuous and coherent ground-level Sequences from single satellite image is highly challenging due the extreme viewpoint gap and inherent spatial ambiguity. We compare our method against three representative baselines: Sat2Density (Qian et al. (2023) ) and ControlS2S (Ze et al. (2025). both designed for cross-view image generation, and EscherNet (Kong al. (2024) recent diffusion-based model for general multiview synthesis.
Since neither the code nor data of Deng et al. (2024); Xu Qin (025 is released, comparisons with them cannot be conducted and thus are not included. StreetScape (Deng et al. (2024)) relies on ground-truth height maps, which indeed simplifies the problem but also creates a strong dependency on data that is often unavailable costly to acquire. Sa12GruundScape (Xu & Qin (2025) requires multiple satellite views to reconstruct 3D model and uses a two-stage generation process, making dependent reconstruction quality and computationally expensive. In contrast, only a single satellite image as input and generates the entire sequence end-to-end. We introduce triplane representation encode the satellite scene and Ray-Based Pixel Attention module
method takes
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Table 3: Application to the downstream cross-view localization task. Experimental evaluation the VIGOR dataset reveals the average localization error before and after synthetic data training.
Aligned JUnaligned
w/o synth datal w/ Ours
5.22
5.33
4.99
5.11
Figure 6:
Qualitative comparison without (top) and with (middle) the proposed epipolar-comee constrained attention.
orner attert
Table 4: Comparison of Full Cross-Attention and Epipolar- Constrained Temporal Attention for realism and temporal consistency.
LFID JDINO [Depth] JFVD JCLIPSIM
w/ Full Cross-Att
42.60
4.253
6.231
2.150
7.516
w/ Epipolar-Att 27.41
4.156
5.623
2.101
6.820
Figure 7: Memory comparison when gen-
crating different frame numbers in a video.
to explicitly enforce geometric consistency between the generated ground views and the satellite image. Furthermore, instead of using standard full cross-attention for multi-view consistency like Sat2GroundScape, we propose an epipolar-guided attention mechanism specifically designed for panoramic images, which greatly reduces computational cost (Figure (7) while achieving superior satellite-to-ground consistency and multi-view consistency (Table
Moreover, their settings rely on multiple satellite images from different viewpoints or real ground-truth height maps, while ours requires only single overhead satellite image as input.
Among these baselines. Sat2Density represents Nerf-based method for cross-view generation. ControlS2S is a recent diffusion based method that synthesizes ground-level images conditioned on single satellite image. EscherNet is state-of-the-as diffusion framework for multiview image generation. To adapt EscherNet to our satellite-to to-ground task, we treat the satellite image as the reference view and assign it virtual camera pose positioned above the image center and oriented vertically downward. Because satellite imagery is approximately orthographic, we model this virtual camera as being located at a high altitude to approximate its projection. Each ground-view camera pose in the trajectory is then expressed reference pose. This formulation provides reference-targe target pose pairs in exactly the format required by EscherNet. For fair comparison, retrain all methods on proposed VIGOR+ dataset.
relative transformation with respect to this satellite
As shown in Table I EscherNet performs the worst across perceptual, semantic, pixel-wise, and depth-consistency metrics, mainly because it lacks an explicit mechanism to bridge the large domain gap between satellite and ground views. However, it achieves better multiview consistency (measured by FVD and CLIPSIM) than Sat2Density and ControlS2S, owing to its built-in multi-view coherence modeling. In contrast, SatDreamer360 explicitly addresses both cross-view appearance disparity and the challenge of multiview continuity. As a result, it achieves the best overall performance across all dimensions, combining high image fidelity with smooth and consistent video generation. Qualitative results Figure 4 further support these findings. EscherNet, which relies on implicit scene encoding, struggles to produce realistic ground-level images. ControlS2S, as illustrated in Figure D lacks effective mechanisms for multiview consistency, leading to spatial discontinuities across frames. In comparison, SatDreamer360 preserves the underlying scene layout and produces ground-view sequences that are both spatially coherent and temporally smooth.
4.2 MODEL ANALYSIS
Our method consists of two key components: (1) a ray guided cross-view feature conditioning mechanism that ensures geometric consistency between the satellite image and the generated ground views, and (2) an frames in the generated ground-view sequences.
attention module that enforces multi-view consistency across

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To validate the effectiveness of the proposed Ray-Guided Cross-view Feature Conditioning Mechanism, we conduct single-imag satellite-to -oogiiuuo- generation experiments on CVUSA and VIGOR, removing the influence of sequential modeling and allowing direct comparison with state-of- the-art methods. We compare our method with Pix2Pix (Isola et al. (2017b)) S2S (Shi et al. (2022)) )
Sat2Density (Qian et al. ), and ControlS2S (Ze et. (2025). while CrossDiff Li et al. (202a)
results are cited from the original paper. Note that pixel-wise metrics may not fully capture the quality
of synthesized images in this task, as they are sensitive factors such as lighting and sky appearance that are not explicitly modeled in satellite imagery. Quantitative and qualitative comparisons on both datasets (Table 2 Figure ) clearly demonstrate that our method outperforms existing approaches in terms of overall generation quality and exhibits stronger left-right boundary consistency across the synthesized ground views. This performance gain primarily results from the proposed Ray-Based Pixel Attention module, which samples features along each viewing ray and explicitly incorporates geometric information from the satellite image. By doing so, the module ensures that the generated ground views are accurately aligned with the underlying satellite- view representation, preserving both local details and global spatial structure, and effectively mitigating artifacts that typically arise in boundary regions complex geometries.
Next, we verify the necessity of the proposed Epipolar-Constrainee Temporal Attention. As shown in Figure 6 incorporating this module significantly improves sequence consistency. Furthermore, Table 4 and F
Attention with full cross- attention substantially increases computational cost, whereas our approach achieves both lower cost and stronger sequence consistency. The ad advantage stems from introducing geometric priors via epipolar geometry, which filters out irrelevant matches, suppresses noise propagation, and eliminates a large number of non-corresponding points before attention computation. Additionally, our sparse interframe attention, which each frame attends only to its immediate neighbors, allows the model to scale efficiently to longer sequences without compromising performance.
7 demonstrate that replacing Spiipola-Coostrriine
Application to Downstream Cross-View Localization Task. SatDream360 can be leveraged to gen- erate synthetic ground-view data from satellite imagery, enabling enhanced training for downstream tasks. We evaluate this benefit in the context of cross-view localization using the state-of of-th-r G2SWeakly (Shi et al. (2024)) model as a baseline. To ensure fair comparison, we follow the same training configuration as the baseline: 10 epochs with identical batch sizes. The only modification is the inclusion of SatDream360 -edeereed data for training augmentation. As shown in Table 3 the augmented model achieves superior performance, demonstrating that the high-fidelite geonetrically consistent samples produced by SatDream360 provide meaningful improvements for cross-view localization tasks.
CONCLUSION
We propose a novel framework for toggrue multiview generation, addressing the chal- lenging task of synthesizing continuous ground-level panoramas from single top-down satellite image. Our approach tackles both spatial and multiview consistency through two key modules: (1) a Ray-Guided Cross-View Feature Conditioning mechanism for accurately constructing satellite-and- ground-view correspondences, and (2) a Multi-scale Spipolar-Constranned Attention module that ensures multiview consistency with significantly reduced computational cost compared standard cross-attention. To facilitate evaluation, we introduce VIGOR++, a large-scale benchmark dataset of aligned panoramic sequences and satellite views. Extensive experiments across multiple metrics and datasets demonstrate that our method outperforms state-of-the-ar baselines in perceptual realism, semantic consistency, and multiview stability. We believe that this work provides strong founda- tion for future research in cross-view generative modeling, with broad potential applications in 3D reconstruction, autonomous driving, and simulation environments.
REPRODUCIBILITY STATEMENT
The implementation details of our model are provided Section 3 with training settings and evaluation protocols provided in Section 4 and Appendix A.7 Additional ablation studies included in the Appendix A.8 A.9 A.II to clarify the effect of individual components. We promise to release both the dataset and the code to facilitate reproducibility.
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A APPENDIX
A.1 THE USE OF LARGE LANGUAGE MODELS (LLMS)
Large Language Models (LLMs) were employed solely as writing and editing assistants during manuscript preparation. In particular, we used an LLM to refine language, improve readability, and enhance clarity across various sections. Its contributions included tasks such as sentence rephrasing. grammar correction, and improving the overall coherence and flow of the text.
Importantly, the LLM played no role in the conception of research ideas, methodological design, experimental execution. All core concepts, analyses, and results were entirely developed and validated by the authors. The LLM's involvement was strictly limited to linguistic refinement and did not influence the scientific content data interpretation. The authors take full responsibility for the entirety the manuscript, including any portions polished with LLM assistance. We have ensured that all LLM-generated content adheres to ethical standards and does contribute plagiarism or scientific misconduct.
0
(16,0')
A.2 EXPLANATION OF PROJECTION GEOMETRY
We provide an explanation of Eq. describing the cor- respondence between pixel coordinates in the camera coordinate system and the angles of the rays found. Any point (u, v) in the pixel coordinate system corresponds to a camera ray with angles (t), 0). where v is the yaw an- gle ranging from [- л and 0 the pitch angle ranging from /2, π /2). For example, as shown in Figure the pixel at coordinates (u', v') corresponds to the fol- lowing ray angles:
Figure 8: Correspondence between image
pixel coordinates and camera ray angles.
Ver (u' マ
/ x 2T, Own (
- x n
(8)
where H and W are the height and width of the panoramic image.
A.3 PROOF OF BQ. 6
Prepare. For vectors [a1 02 03) defined as:
T and b
[b1 by by]
the outer product of the two vectors is
Tazby ayb²
0
03
a22
a &
0z61 a1 by
a3
0
01
b åb.
La b2 azhh
02
01
0
Here, we introduce the skew-symmetric matrix notation, à, which corresponds to the vector a. Using this notation, the outer product b is expressed compactly the matrix-vector product âb.
Proof. Consider the ground images of frames m and n. there is spatial point Q = (I, y, z) in the coordinate system of frame m corresponds pixel points g on frame m and go frame n in panoramic images: These projections are given by:
gh. = P(Q), Sh w = P( ((Rmm Q + fmm):
(10)
where P denotes the equirectangular camera projection transformation, Rmn and tmn denote the rel- ative rotation and translation between frames and Perform the inverse projection transformation to obtain:
(o Q. (o R Q tyan
(11)
Substitute Q from the left equation into the right gives:
p- p- (g Rmn (P- (ot Imn-
(12)
By simultaneously left multiplying with skew-symmetrie matrix ina (corresponding to 4mm), as introduced in Eq. 9
Emn (P- (P-1(0) w. iim Pemm (P-1-1 (9tH
(13)
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Next, multiply both sides on the left by the transpose (p (p (n
(p 3) (P- ( ((it,w)))) = (P- (g ))² Emn Rmn (P-1 (g (),
(14)
Since the Product(P-1(C) )) " (P (o"' (r)))r)) = 0 (because Emm ( (P- (Du w.) orthogonal to P- (Dw D), the above simplifies to:
(P (g" w))) ) fm Rmm! (P (P- = 0.
(15)
This result implies that pixels Dx. on frame m and g of on frame n that correspond to the same Therefore, during temporal attention,
spatial point must satisfy this constraint relationship in Eq. 6 the points Dh.yy on frame m only need to focus on the set of points (g } on frame n that satisfy the above constraint. This significantly reduces the computational complexity compared to focusing all pixels in the image.
A.4
IN PANORAMIC IMAGES
Figure 9: (A) Example of camera positions ps and Pr. (B) Selected point on source image plane Ps. (C) Epipolar ray projected onto target image plane pr. (D) Simplified epipolar curve after removing in-iiterreectng portions. (E) potential correspondences refined by depth constraints.
In Figure we show the full derivation of the panoramic epipolar geometry. Taking the camera positions pd and p¹ shown in Figure 9 A) as an example, we consider the red point on the image plane of pd in Figure(/ As illustrated Figure CC) because p² uses an equirectangular projection, the ray corresponding to this point on p is projected onto the image plane of p² as a green wavy curve. Since the back side of the sphere does intersect with this ray, can be omitted, resulting in the simplified curve shown in Figure ID). Moreover, the valid portion of the ray from p² must lie in front of the camera, so points corresponding to negative ray directions can be removed. This yields the final green line segment in Figure[]E) which represents the valid epipolar region. The true correspondence of the red point on p² must lie on this green segment.
By using this formulation, we no longer need to compare every point on the image plane of pd with all points on p². Instead, attention is restricted to the green line segment Figure This significantly reduces computational complexity and avoids introducing noise through interactions with irrelevant points.
A.5 SCALING TO LARGE-SCALE SCENES
As described in Section 3 the proposed model leverages Ray-Guided Cross- View Feature Condition- ing and Eipipolar-Constrainee
Attention to generate ground- view images aligned with the satellite inputs and consistent across views. However, scaling the approach to larger scenes remains chal- lenging. Due the inherent stochasticity of diffusion models, different batches conditioned on the same satellite image can produce noticeably different results, as illustrated in the first two rows of Figure TO This highlights the need to establish connections across images generated from multiple batches to enhance the scalability of scene generation.
To incorporate information from previously generated images into the upcoming sequence, update the triplane representation using Image Cruss-Attention (ICA Li et al. (2024c)), which enriches each point the XY, XZ, and YZ planes by referencing corresponding pixels from previously generated
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Trajectory
Trajectory 2
Trajectory + Update the triplane with images generated in Trajectory
Figure 10: Using the satellite image condition, different trajectories are input generate correspond- ing images. From top bottom: results for trajectory I, results for trajectory 2, and results for trajectory after updating the triplane with images generated from trajectory 1. Updating the triplane ensures that newly generated results are related to prior sequences.
images. For example, points on the XY plane are sampled along the Z-axis, projected into camera space, and aligned with image coordinates, enabling feature transfer through ICA:
Ftop
ICA
Looo Ref2y
Ref = 1.
(16)
Here, (u, 12) (c, 2,) t2) projects sampled 3D points into the j-th image plane via
rotation Rj. translation i, and projection P. where ((x, y, x)) represents the set of sampled points along the Z-axis. By integrating ICA with CVHA, the triplane accumulates information from prior results, yielding coherent and scalable scene generation across multiple batches (Figure [0] bottom row). By combining ICA and CVHA mechanisms, the triplane's 's features aggregation of information from previously generated images. As shown at the bottom of Figure I0 this approach enables the generation of extended image sequences across multiple batches, ensuring coherence and continuity in the outputs.
enriched through the
A.6 DETAILS OF VIGOR+
The VIGOR+ dataset includes 91,498 satellite images and the same number of street-view images, as shown in Figure Ia).. These images are evenly distributed across ten cities, covering a total area of 17.47 km?
1200
400
2M
Number Samples perCrty
(1 Number of Sangles Trpectry
Figure 11: (a) Distribution of the Number of Samples Across Cities. (b) Distribution of Trajectory Sample Counts.
To construct trajectories, we first associate each ground-view image with its corresponding satellite patch, where the ground-view image lies at the center. Using this center point as the seed, we first filter out images captured under different weather conditions temporal consistency. We then construct a connectivity graph and apply depth- first search to extract all feasible routes. A semi-automatic procedure, combined with manual verification, then selects the longest and most temporally consistent route. Through this process, we obtain one trajectory for each satellite image.
different dates, these frames lack
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Overall, generate over 90,000 cross view satellite -atellitegrouud video pairs. Among them, 84,055 pairs are used for training and 7,443 pairs are used for testing. As shown in Figure [T).. most trajectories consist of to 16 ground-view frames. The average frame interval is approximately 11 m, with the smallest interval being 0.079 m and the largest reaching 20 m.
A.7 ADDITIONAL EXPERIMENT DETAILS
Our model is fine-tuned based on the pre-trained Stable Diffusion 1.5 model (Rombach et al. (2022) with the training process divided into three stages.
Initially, we train on single-image generation tasks with a batch size of 32 for 300 epochs, focusing on adapting the parameters of Ray-Guided Cross- Feature Conditioning to generate ground-level images consistent with satellite geometry.
Next, we incorporate the Epipolar-Constrainee Temporal Attention module to enable continuous ground scene generation This phase involves a total of 300 epochs. The first 5 epochs focus on pre-heating the Temporal Attention module with a batch size of 8 and 3 images per sequence. This is followed by fine-tuning the entire model for 200 epochs with the same batch size and sequence length, and then further fine-tuning for long sequence generation with batch size of 4 and 5 images per sequence until reaching 300 epochs.
As the Autoencoder when handling a temporally coherent sequence of images. Same to (Blattmann et al. (2023b)), we maintain the original parameters and introduce temporal modules (3D convolution) in the decoder. The Autoencoder is trained on VIGOR+ with a batch size of 4 and 3 images per sequence for 40 epochs, focusing solely on training the added temporal module.
originally trained only single-image tasks, it caused flickering artifacts
Throughout the training process, the learning rate is set to 7.0c-05, the optimizer used is AdamW, and all experiments are conducted on four NVIDIA L40 GPUs. This comprehensive training pipeline enables the model to generate geometrically accurate ground-level level images and seamless, temporally coherent sequences.
A.8 ABLATION STUDY ON SCENE REPRESENTATIONS
The comparison between triplane and BEV representations is presented in Table where the BEV corresponds to using only the Y-planee from the triplane. The memory usage is measured with batch size of 32.
Table 5: Quantitative comparison of Triplane and BEV(XY-planee) representations on the VIGOR dataset.
Pater
IDIN
4SegAny
1SSIM
*PSNR
SSD
JDepth
Memory
BEV Triplane
0.3803
4.408
0.3549
0.4134
13.64
12.94
7.061
19011
0.3469
4.287
0.3471
0.4385
14.08
13.11
6.727
20303
These results show that the triplane representation consistently outperforms the BEV (XY-plane) representation across all metrics, while introducing only modest increase in computational overhead. The improvement comes from the triplane's ability to capture richer 3D structures. A pure XY-plane representation lacks vertical information, which is critical for rendering views under varying pitch angles. Moreover, our model samples features along camera rays. Relying solely on XY-plane features leads to incomplete spatial support, especially for oblique rays. The triplane effectively overcomes this limitation.
A.9 ABLATION STUDY ON RAY-GUIDED CONDITION
To validate the impact of the ray-guided conditioning mechanism, we compare our method with a vanilla conditioning approach, which performs cross-attention between every 3D point feature in the triplane and each image pixel. The corresponding ablation results on VIGOR are shown in Figure 6
The ray- ray-gddded conditioning substantially improves performance. This is because vanilla conditioning lacks explicit geometric constraints, often resulting in geometric distortions, whereas ray-guided conditioning leverages Ray-Based Pixel Attention dynamically sample points along viewing rays
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Table 6: Quantitative comparison of vanilla Cross-Attention Ray-Based Pixel Attention on VIGOR dataset.
Pater
IDINO
[SegAny
1SSIM
*PSNR
1SD
,Depth
J'Tim((s)
[Memory(MB)
Vanilla Condition
0.5413
5.425
0.3911
0.3174
12.35
12.05
25.38
22506 120.28
Ray-guided condition
0.3469
4.287
0.3471
0.4385
14.08
13.11
6.727
39.64
18296
using learnable offsets (Eq. 5). This approach ensures spatial coherence and improves geometric aligmment across views.
Furthermore, we conduct an ablation study on the Dynamic Refinement of Offsets in Ray-Based Pixel Attention, with results shown in T
Table Ablation study on the effect of dynamic refinement tested on the CVUSA dataset.
Paice
IDINO
JSegAny
1SSIM
*PSNR
1SD
[Depth
w/o Dynamic
0.4647
4.935
0.3639
0.3736
13,45
12.38
19.69
w/ Dynamic
0.4255
4.807
0.3602
0.3812
13.88
12.42
19.36
The goal of Ray-Based Pixel Attention is to aggregate meaningful features for each ground-level pixel by sampling along its corresponding 3D ray projected into the triplane. However, Early in the diffusion process, the latent features are dominated by noise, making accurate 3D correspondence difficult. To address this, we begin with uniform sampling along each ray. But uniform sampling often leads sparse or suboptimal feature aggregation, leading to degraded performance, as shown in the first row of Table
To mitigate this, we propose mechanism to dynamically refine both the sampling offisets and their corresponding weights during diffusion inference. As denoising progresses, latent features gradually capture meaningful scene structure. The model uses these evolving features to predict offsets and weights for each sampling point along the ray, enabling more accurate and view-consistent feature aggregation. This leads improved performance, as shown in the second of Table 7
A. ABLATION STUDY ON EPIPOLAR-CONSSS CONSTRAINDD ATTENTION
To more thoroughly evaluate the contribution of the Epipolar-Constrainee Attention mechanism to geometric consistency, we extend our ablation studies by removing this component and comparing it against a vanilla inter-frame attention baseline (Full Cross-Att).
Beyond FVD, we additionally measure the average inter-framm similarity using a CLIP-based metric (CLIPSIM). We also conduct user study, where we compute the Average User Ranking (AUR). Specifically, we randomly sample 1,000 trajectories from the test set and collect rankings from five users to obtain the final AUR scores. The results are summarized below:
Table 8: Ablation Study of the Epipolar- Attention on the VIGOR+ Dataset.
JDINO
[SegAny
*PSNR
DDepth
JFVD
JCLIPSIM
tAUR(seg)
w/o Epipolar-AN Att
4.2748
0.3626
12.86
6.174
3,439
10.20
0.174
w/ Full Cross-Att
4.253
0.3589
12.75
6,231
2.150
7.516
1.136
w/ Epipolar-Att
4.156
0.3563
12.75
5.623
2.101
6.820
1.690
These results demonstrate that adding Constrir- Attention yields a substantial improve- ment over the variant without it. Compared with the vanilla inter-frame attention, our method achieves 10% gain in CLIPSIM, receives significantly higher user rankings, and, as shown in Fig. 7, substantially reduces computational resources. Together, these findings provide strong evidence that the proposed Epipollr-Conssrriined Attention effectively enhances inter-frame geometric consistency.
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A.11 ABLATION STUDY ON SPARSE INTERFRAME ATTENTION
We adopt sparse interframe strategy after careful comparison with the dense interframe strategy. Specifically, the sparse strategy queries only the two preceding frames for each target frame, whereas the dense strategy attends to all frames within the sequence. As shown in Table both strategies achieve similar per-frame quality, but the sparse strategy yields better multiview consistency. This because distant frames often contribute less meaningful information and may introduce noise, while nearby frames provide more relevant context.
Table 9; Ablation study the effect of sparse interframe strategy tested on the VIGOR+ dataset.
JFVD
JCLIPSIM
JPatee
IDINO
_Depth
1PSNR
Dense
2,253
7.071
0.4085
4.153
5.791
12.91
Sparse
2.101
6.820
0.3955
4.156
5.623
12.75
We further compare runtime and memory usage as the number frames increases (with batch size set to I and 50 DDIM steps). Table I0 shows that the sparse strategy is significantly more efficient, particularly for longer sequences.
Table 10: Ablation study the resource consumption of the sparse interframe strategy.
Strategy
Metric
10 Frames
20 Frames
30 Frames
Dense
Time(s)
20.33
59.12
120.75 40520
Memory(MB)
26174
31106
Sparse
Time(s)
14.29
22.50
32.71
Memory(MB) 25824
30242
35142
In summary, the sparse inter-frame strategy achieves better multiview consistency while reducing both computation time and memory usage, making it the preferred choice for our generation framework.
A.12 ABLATION STUDY ON SATELLITE ENCODING MODELS
We choose a CNN-based ResNet encoder because our task imposes strong gentiellec con- straints, and the satellite features expected to faithfully preserve the geometric structure of the overhead imagery. CNNs produce feature maps that are spatially continuous and locally coherent, making them naturally suitable for encoding geometry-aware local structures. In contrast, Vision Transformers partition the image into patches and apply global attention, a process that often disrupts the original geometric relationships in satellite imagery.
We also conduct an empirical comparison on the CVUSA dataset using ResNet and ViT for satellite encoding. As shown in the table II the ResNet encoder consistently outperforms ViT across all metrics:
Table II: Ablation study on satellite encoding models.
JP PPsqqueeee
JPaalex
[DINO
[SegAny
1SSIM
PPSNR
SSD
[.Dptth
Vit ResNet
0.3356
0.4469 0.4255
5,049 4,807
0.3711 0.3602
0.3371
13.30
12.11
20.96
0.3146
0,3812
13.88
12.42
19.36
A. 13 INFLUENCE OF TRAJECTORY SEQUENCES
We analyze this effect of trajectory sequences from three perspectives: the trajectory length, the sampling interval, and the relative spatial relationship between the trajectory and the satellite image.
Trajectory length. We reorganize the test set by grouping trajectories based on their total length into four ranges: <20 m. 20-40 m, 40-60 m, and >60 m. The results are shown below. The model performs well for trajectories shorter than 60 m, but we observe noticeable degradation when the trajectory length exceeds 60 m. This limitation primarily arises from inability to train the model
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Table 12: Influence trajectory length.
Distance
JP alex
JDINO
JSegAny
1SSIM
1PSNR
[Depth
JCLIPSIM
<20m
0.3793
4.182
0.3515
0.4230
12.88
5.582
5.947
20-40m
0.3812
4.159
0,3529
0.4141
12.79
5,422
5,921
40-60m
0,3850
4.143
0.3538
0.4072
12.75
5,417
6.065
> 60m
0.3972
4.160
0.3552
0.3839
12.69
5.760
6.744
on sufficiently long sequences due to computational constraints. We expect this issue be mitigated by incorporating longer trajectories during training in future work.
Sampling interval. To examine how sampling density affects generation quality, we reorganize the test set and evaluate trajectories under different frame-interval ranges. Specifically, we group trajectories into two categories: intervals <10 m and intervals m. The evaluation results are shown below. As the frame interval increases, the generation task becomes more challenging because the correlation between adjacent frames becomes significantly weaker. Consequently, we observe a consistent trend of performance degradation at larger sampling distances.
Table 13: Influence of sampling interval.
Interval
JP alex
[DINO
[SegAny
1SSIM
1PSNR
Depth
JCLIPSIM
10m
0.3611
4.088
0.3420
0.4592
13.35
4.304
6.204
10m
0.3848
4.155
0,3530
0,4090
12.82
5.494
6.639
Relative spatial relationship between the trajectory and the satellite image. To evaluate how the relative spatial relationship between the trajectory and the satellite image affects generation quality, we shift the satellite image so that the trajectory appears different locations within it. For example, when the satellite image is translated 20 m to the right, the offset by 20 m. Using this setup, we test offsets of +20 m and +40 m. The results show that the generation quality is almost unaffected by these spatial shifts. This is primarily because our method does not naively use the entire satellite image as global conditioning input. Instead, we extract geometry-aware features through Ray-Based Pixel Attention, which selectively samples informative points from the satellite representation along the ray directions. This sampling- design effectively mitigates sensitivity to the global spatial alignment between the trajectory and the satellite image.
Table 14: Influence of relative spatial relationship between the trajectory and the satellite image.
Offset
JP alex
JDINO
JSegAny
1SSIM
PPSNR
Depth
JCLIPSIM
0.3955
4.156
0.3563
0.3964
12.75
5.623
6.820
+20
0.3955
4.150
0.3560
0.3962
12.78
5.631
6.745
+40
0.3973
4.164
0.3567
0.3959
13.73
5.674
6.706
A.14 HYPERPARAMETER ANALYSIS
The number of ray samples(K). We evaluate the effect of different numbers of ray samples K on the CVUSA dataset using a batch size of 16. The results are shown in Table [T] Although increasing the number of ray samples(K=12) allows the model to capture more features, the improvement over K=8 relatively marginal. Considering that K=8 offers significantly better balance between performance
and memory consumption, adopt K=8 as default setting.
The number of sampled points satisfyin ingg epipolar constraints(M). We evaluate the influence of the number of epipolar sampling points on VIGOR++, and the results are summarized in Table [6 The experiments show that sampling four points already provides a strong geometric prior that effectively improves multi-vied view consistency. Increasing the number of samples offers only marginal gains while introducing substantial additional computational cost. Therefore, we adopt four sampling points as our default setting.
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Table 15: Ablation study on number of ray samples(K).
JP squeeze
J.P alex
JDINO
JSegAny
1SSIM
1PSNR
4
0.3246
0.4386
4.920
0.3670
0.3606
13.62
0.3146
0.4255
4.807
0.3602
0.3812
13.88
0.3109
0.4206
4.807
0.3623
0.3708
13.91
Table 16: Ablation study on the number of sampled points satisfying epipolar constraints(M),
M
JFVD
JCLIPSIM
5.509
7.142
2.101
6.820
3.532
6.770
Triplane resolution. We extract features from 256x256 satellite images to 32x32 resolution, maintaining an 8x downsampling rate consistent with the VAE encoder of the latent diffusion model. Preserving this feature resolution ensures that the triplane conditioning information is spatially aligned with the diffusion latent, which improves conditional guidance and training stability.
Training design. Empirically, the model reaches convergence after 300 epochs in each stage. In the first stage, primarily learns consistency between ground views and the satellite image, while in the second stage, the multi-view consistency. Convergence is assessed based on the clarity and structural coherence of generated samples, absence of obvious noise or artifacts, and stabilization of validation metrics such as SSIM and Patez stabilize. The learning rate is set 7e-05, following the successful practice of ControlS2S, which ensures stable training and consistent convergence. Finally, the batch size is gradually reduced from
Temporal Attention module introduced to facilitate
- 4 across stages due to GPU memory constraints: the number of frames per sequence increases from - 3 - 5, memory consumption rises, necessitating smaller batches to accommodate longer sequences.
A.15
USER STUDY
To provide a more comprehensive evaluation of method, we conduct user study. Specifically, we compute the Average User Ranking (AUR). We randomly sample 1,000 trajectories from the test set and collect rankings from five users obtain the final AUR scores. Each user ranks the methods for each sequence: the top-ranked method receives 2 points, the second receives point, and the third receives points. The final AUR is computed by averaging the scores across users. The evaluation considers two aspects: similarity between the generated ground scenes and satellite images (S2G-Sim) and multi-view consistency (MV-Cons). These results, shown Table I7 demonstrate that our method generates ground scenes that better reflect the satellite imagery and achieves superior multi-view consistency.
Table 17: User study.
SS2G-Sim
*MV-Cons
EscherNet ControlS2S
0.481
0.974
1.057
0.051
Ours
1.462
1.975
A.16 EVALUATION OF GENERATION RESULTS UNDER DIFFERENT RANDOM SEEDS
To avoid potential bias, conduct additional tests under different random noise seeds. On the VIGOR+ dataset, we evaluate our method using seeds 1, 25, 50, and 75. The corresponding results presented in Table 18
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Table 18: Evaluation of generation results under different random seeds.
Seed
JPP alex
LFIID
[DINO
JSegAny
1SSIM
PPSNR
_Deptth
JFVD
JCLIPSIM
1
0.3955
27.39
4.157
0.3565
0.3965
12.75
5.616
2.127
6,815
25
0.3955
27.41
4.156
0.3563
0.3964
12.75
5.623
2.101
6.820
50
0.3954
27.52
4.155
0,3562
0.3966
12.76
5.620
2.126
6.798
75
0.3958
27.41
4.157
0.3564
0.3964
12.76
5.637
2.109
6.843
Average Std
0.3956
27.43
4.156
0,3564
0.3965
12.76
5.624
2.116
6.819
0.0002
0.0591
0.0010
0.0001
0,0001
0.0058
0.0091
0.0128
0.0186
Figure 12: Failure case.
A.17 LIMITATIONS AND DISCUSSIONS
While SatDreamer360 can generate continuous ground scene images from a single satellite image and given ground camera trajectory, it still faces several limitations and corresponding areas for improvement.
Although VIGOR+ covers diverse regions, it is still constrained by Google Maps coverage and existing road networks, which may limit generalization to off-road or unstructured environments. As shown in Figure 12 our method occasionally produces incorrect results when generating scenes in narrow alleyways that are not accessible to vehicles. Future work will incorporate additional data sources such as drone imagery, expand coverage and enhance the model's generalization ability.
cameras, or crowd-soureed panoramic data to
Additionally, Our method focuses primarily on modeling the static components of the scene and does not explicitly handle dynamic objects such as vehicles and pedestrians. As result, the generated sequences may lack realistic dynamic behaviors. In future work, we plan to incorporate dynamic objects to establish more realistic satellite-to-grone correspondences.
A.18 BROADER SOCIAL IMPACTS
Our proposed system, SatDreamer360, can generate continuous ground scene images from a single satellite image and a given ground camera trajectory, making it valuable tool for applications such as 3D reconstruction, simulation, and autonomous navigation. However, while it can produce visually plausible ground scenes, it still struggles to capture all real-world details, and caution should be exercised when deploying it safety-critical scenarios.
Moreover, like many generative models, SatDreamer360 could be misused to synthesize misleading or fake visual content. To mitigate such risks, we recommend using only in controlled research industrial settings, incorporating usage licenses and watermarking techniques trace generated content, and clearly disclosing when images are synthetic. These safeguards can help prevent misuse and ensure that the technology is applied responsibly.
A.19 EXAMPLES OF SATELLITE IMAGES AND TRAJECTORIES
Figure 13 presents example satellite images along with their corresponding trajectories, corresponding to the figure shown in the main text.
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Trajectory 1 (TI)
Trajectory (T2)
Figure 13: Satellite images and corresponding trajectory points.
A. MORE VISUALIZATION RESULTS
In Figure 14, we provide additional visualization results. Our method accurately follows the geometric layout of the satellite imagery while maintaining strong multiview consistency. It achieves robust performance both densely built urban areas and rural regions. Even challenging turning scenarios (middle of the figure), the generated results exhibit good continuity.
To further evaluate the generalization capability of our method, we our model on cities in Africa, and Europe, despite training exclusively on U.S. cities. Under this challenging cross-continent setting, as shown Figure 15 and Figure T6 other methods often fail, whereas our approach consistently produces plausible and coherent results, demonstrating strong generalization ability. This robustness stems from the tri-plane representation, which captures both horizontal and vertical structural cues across multiple orthogonal planes, providing sufficient geometric support for a wide variety of urban environments. Moreover, our Ray-Based Pixel Attention effectively aligns the ground-vien generation with the satellite representation, enabling the model to maintain geometric consistency with the satellite imagery across diverse city layouts.
To evaluate the model's generation capability on long trajectories, we provide additional visualizations in Figure 17 The images are arranged from left to right and top to bottom, showing the generated results of vehicle navigating a left-turn scenario. The entire trajectory spans approximately 70 meters, and the results demonstrate that our method maintains high geometric consistency even for longer and curved paths.
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ExcherNett ControlS2S
Ours
GT
EscherNet ControlS2S
Ours
GT
EscherNet ControS2S
Ours
GT
Figure 14: Qualitative comparison of ground-level image sequences along trajectories. The left shows the satellite image and the corresponding trajectory. while the ground-level images progress along the trajectory from left right.
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FacherNet ControlS2S
Ours
GT
Europe (51"2948.5") (70432,5")
Figure 15: Results of ground-view generation using European satellite images as input.
EscherNet ControIS2S
Ous
GT
Arica 28'0433.2'))
Figure 16: Results of ground- generation using African satellite images input.
9
10
11
Figure 17: Generation results on long trajectories, where the images are ordered from left right and top to bottom following the vehicle's forward motion.
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Reviews:
[{"review_id": "IKdv4WynyL", "reviewer": "Reviewer_DioS", "summary": "This paper introduces SatDreamer360, a generative framework capable of producing multi-frame geometrically consistent ground-level panoramic image sequences from a single satellite image and a predefined camera trajectory. For single-frame generation, the framework employs a Triplane representation and a ray-based pixel attention mechanism to enhance geometric fidelity and visual realism. To ensure spatial and temporal coherence across generated frames, the authors further propose a Panoramic Epipolar-Constrained Attention module, which explicitly models geometric relationships between different views. To evaluate the proposed one-to-many sequence generation task more comprehensively, the authors introduce the VIGOR++ dataset, which extends the original VIGOR dataset by incorporating additional cities and continuous ground-view trajectories, enabling a more complete assessment of the framework’s performance. Overall, the proposed panoramic sequence generation method significantly improves the geometric consistency and continuity of satellite-to-ground image synthesis, providing higher-quality data for downstream applications.", "questions": "1. It is recommended that the authors include a more detailed description of the dataset in the supplementary materials, such as the number of satellite and street-view images, the number of trajectories corresponding to each satellite image, and the distribution of the dataset across different cities.\n\n2. Considering that the trajectory information is an essential component of the model input and directly governs the generated sequence outputs, I suggest that the authors further discuss the design of trajectory sequences. Specifically, how long the trajectories can be supported, how the sampling interval influences the generation results (as the current discussion only covers short sequences), and whether the relative spatial relationship between the trajectory and the satellite image affects the generation results.\n\n3. Related to the previous point, the current experimental results mainly involve short and nearly straight trajectories. I am curious whether the proposed method can maintain strong geometric consistency when dealing with longer or curved trajectories, especially at intersections or turning points in urban environments.\n\n4. The current visualizations effectively demonstrate the model’s strong multi-view geometric consistency. However, including some failure cases in the supplementary materials and analyzing the underlying reasons would make the framework more convincing.\n\n5. In Figure 2, could the description of the EPIPOLAR-CONSTRAINED ATTENTION be made clearer? The explanation on the right side of this figure appears somewhat confusing.\n\n6. A concern raised by the second limitation is whether the tri-plane representation can adequately replace traditional 3D representations in capturing more complex vertical spatial structures or non-planar terrains. Considering that the dataset primarily focuses on major U.S. cities with relatively flat terrain and similar urban layouts, it would be beneficial to include inference experiments on cities from Asia, Africa, or Europe, even if ground-truth references are not available.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Introducing image sequence generation into the satellite-to-ground view image synthesis task enables geometrically consistent sequential images, which are more suitable for downstream applications such as autonomous driving and 3D reconstruction. This approach further enhances the practical value of cross-view generation.\n\n2. The proposed ray-based pixel attention mechanism and the epipolar-constrained attention leverage the geometric priors inherent in the imaging process, thereby improving the interpretability of both the module and the overall framework.\n\n3. The authors constructed the VIGOR++ dataset, which not only increases the diversity of data types by expanding the number of cities but also provides carefully processed street-view image trajectories. This dataset offers a comprehensive and reliable benchmark for the proposed one-to-many sequence generation task and holds great potential value in other fields such as urban perception, autonomous driving, and 3D reconstruction.\n\n4. The authors conducted extensive and thorough experiments to validate their framework, reflected in the use of diverse evaluation metrics across multiple levels and detailed ablation studies for each module. The supplementary materials provide additional methodological details and experimental analyses, and the evaluation of the generated results in downstream tasks is particularly commendable.", "weaknesses": "1. The proposed sequence generation task relies on a single satellite image and predefined trajectory inputs. However, the authors provide insufficient introduction and discussion regarding the trajectory data, including details such as the number of frames within each trajectory, the spatial intervals between frames, and the relative positioning of the trajectories within the satellite imagery.\n\n2. Although the proposed VIGOR++ dataset expands the number of cities to enhance data diversity, it remains limited to U.S. cities. The absence of data from non-U.S. regions such as Asia and Europe may pose potential limitations to the model’s generalization capability.", "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": "2Fo0Yeeq7w", "reviewer": "Reviewer_7M3F", "summary": "This paper introduces a diffusion-based framework capable of generating sequential ground-view images conditioned on a given satellite image and trajectory. The method employs a ray-based pixel attention mechanism to effectively aggregate features from a triplane representation. Furthermore, an Epipolar-Constrained Attention module is incorporated to enforce geometric consistency across multiple camera views. In addition, the authors release a new dataset establishing correspondences between satellite maps and ground-view sequences.", "questions": "1. What is the purpose of using a triplane representation? From the ablation studies (Table 5), it seems that the performance gain is not very significant even with increased computational resources. Moreover, since satellite images only provide 2D information, how can the features along the Z-axis in the triplane representation be effectively extracted without height information?\n\n2. When constructing the dataset, how to determine the sampling density of ground images? Does the sampling density (high or low) affect the quality of the generated images?\n\n3. How does the system deal with the dynamic object issue during the image generation process?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "* The paper’s objectives are novel and clearly defined. \n* The proposed approach is logically sound, and the contributions are substantial — including the introduction of a new dataset for a newly defined task. \n* The experiment demonstrates the effectiveness of the proposed method with state-of-the-art performance.", "weaknesses": "* Some parts of the paper are not fully explained and require further clarification from the authors (Details shown in the Question part).\n* The epipolar constraint only ensures local consistency between two frames rather than global consistency, which makes the overall constraint relatively weak.\n* There aren’t enough experiments to examine how each module affects the consistency metric, which I personally consider a core metric for multi-image generation. The existing ablations—for example, comparing full cross-attention with epipolar attention—some consistency metrics (e.g., FVD), the enhancement does not seem significant. The authors need to include more comprehensive evaluations to substantiate their claim of enhancing consistency performance..", "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": "2DfSvlq0Kq", "reviewer": "Reviewer_QDgj", "summary": "This paper introduces SatDreamer360, a diffusion-based framework that generates multiview-consistent ground-level panoramas conditioned on a single satellite image.\nThe model integrates three main components:\nRay-guided cross-view feature conditioning, leveraging a triplane representation to model geometric correspondences between satellite and ground perspectives.\nEpipolar-constrained attention, enforcing temporal and geometric consistency across panoramic frames using equirectangular projection geometry.\nA new dataset, VIGOR++, extending the VIGOR dataset with continuous ground-level sequences and trajectory annotations for evaluating satellite-to-ground synthesis.", "questions": "1. Ablation completeness:\nCan you provide quantitative ablation results isolating the effects of (a) the triplane representation, (b) the ray-guided conditioning, and (c) the epipolar attention? How much performance gain does each contribute individually?\n2. Did you conduct any small-scale user study or human preference comparison? If not, could you report preliminary results or plan to include one in the final version?\n3. Since VIGOR++ uses Google Street View imagery, how is licensing and privacy handled?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tSatellite-to-ground image generation is a challenging and underexplored topic. It has practical value for simulation, urban modeling, and cross-view localization.  \n2.\tThe combination of triplane representation, ray-guided sampling, and epipolar attention is technically sound. The pipeline effectively bridges satellite conditioning and panoramic generation.\n3.\tThe results show noticeable improvements in both image realism and geometric coherence. The qualitative examples look visually convincing.", "weaknesses": "1.\tThe core architectural ideas—triplane representation (EG3D), ray-based sampling (MVDream, Zero123++), and epipolar-constrained attention (EpiDiff)—are largely from prior work. SatDreamer360 primarily integrates these components within a diffusion framework for a new application. While the system integration is well-executed, it lacks a fundamentally new algorithmic or theoretical contribution.\n2.\tAblation and analysis are not deep enough. It’s unclear how much each proposed module contributes. The paper would benefit from more detailed ablation and efficiency studies.\n3.\tSince the task involves generative image synthesis, some form of human preference or perceptual realism test would strengthen the claims.", "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": "mApujZ2tv9", "reviewer": "Reviewer_5Zje", "summary": "This paper proposes a framework for generating multiview-consistent 360° ground-level panoramic sequences from a single satellite image and a predefined camera trajectory. The two key methodological contributions are: 1. ray-guided cross-view feature conditioning (a ray-based pixel attention mechanism samples features along viewing rays, establishing explicit geometric correspondences between satellite and ground views), 2. epipolar-constrained attention for panoramas (an attention mechanism that enforces multiview consistency across frames by restricting attention to geometrically valid correspondences, reducing computational complexity). The paper also introduces the VIGOR++ Dataset. Experiments demonstrate state-of-the-art performance across perceptual, semantic, pixel-level, and multiview consistency metrics, outperforming methods like Sat2Density, ControlS2S, and EscherNet.", "questions": "Based on my comments above, questions that the authors could respond to in the rebuttal:\n\n1. Statistical significance of results\n2. Deeper methodological comparitive discussion to Deng et al. (2024) and Xu & Qin (2025)\n3. Justification of hyperparameter choices\n4. Sparse interframe attention - why 2 frames?\n5. Provide additional EscherNet adaptation details\n6. Why ResNet for satellite encoding? Have you tested modern alternatives like Vision Transformer?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Novel Problem Formulation: While satellite-to-ground synthesis exists, the paper uniquely formulates the problem as generating geometrically consistent multiview sequences from a single satellite image with trajectory control. This is more challenging and practical than prior single-view or multi-satellite approaches. Most work targets perspective images; the consistent treatment of equirectangular projections throughout the pipeline is less explored.\n\nRay-based pixel attention with learnable offsets: Starting with uniform sampling and progressively learning offsets and weights as denoising proceeds is an elegant solution to the \"noisy latent\" problem in early diffusion steps. This bridges geometric sampling with diffusion's iterative nature.\n\nExtending epipolar geometry from pinhole to equirectangular projections and integrating into attention mechanism requires non-trivial mathematical reformulation.\n\nTriplane + diffusion integration: While triplanes exist, conditioning latent diffusion with ray-sampled triplane features (rather than global or BEV features) is novel. The cross-view hybrid attention for enriching planes with orthogonal projections adds spatial reasoning.\n\nVIGOR++ represents substantial effort: 90K+ pairs, 10 cities, semi-automatic trajectory construction with manual refinement. The methodology for building continuous sequences from Street View (connectivity graphs, DFS path extraction) shows engineering creativity.", "weaknesses": "It is unfortunate that Deng et al. (2024) and Xu & Qin (2025) do not provide source code yet. They both appear quite strongly relevant so further discussion of input requirements (single vs. multi-view satellite), architectural differences, computational costs, and theoretical advantages would improve the paper.\n\nMethods like Stable Video Diffusion or AnimateDiff could be adapted by conditioning on satellite images. These would test whether the geometric reasoning actually provides value over general temporal coherence mechanisms.\n\nEscherNet seems like a weak baseline comparison. It is designed for object-centric multiview synthesis, not cross-view scene generation. The paper states they \"adapt it to our setting by treating the satellite image as the source view\" but provides no implementation details. This adaptation may be fundamentally disadvantaging EscherNet.\n\nAll metrics reported as single numbers without error bars, confidence intervals, or variance. Diffusion models are stochastic - different random seeds produce different results. So it would be good to have some sense of whether results are statistically significantly better.\n\nThere are quite a few hyperparameters without strongly justified values:\nK (number of ray samples): value never specified in main paper\nM (epipolar constraint points): \"M ≪ HW\" but exact value unclear\nWhy 2 preceding frames in sparse attention? No ablation on alternatives\nTriplane resolution not specified until limitations section\nWhy 300 epochs for each stage? Was convergence analyzed?\nLearning rate 7e-05 - was this tuned or borrowed from SD1.5?\nWhy batch size 32 → 8 → 4 across stages?", "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)