PanoWan: Lifting Diffusion Video Generation Models to 360$^\circ$ with Latitude/Longitude-aware Mechanisms
Authors: Yifei Xia
Shuchen Weng
Siqi Yang
Jingqi Liu
Chengxuan Zhu
Minggui Teng
Zijian Jia
Han Jiang
Boxin Shi
Venue: NeurIPS 2025 poster ()
Keywords: Panoramic Video Generation
Video Diffusion Models
Primary area: deep_learning

Abstract: Panoramic video generation enables immersive 360$^\circ$ content creation, valuable in applications that demand scene-consistent world exploration. However, existing panoramic video generation models struggle to leverage pre-trained generative priors from conventional text-to-video models for high-quality and diverse panoramic videos generation, due to limited dataset scale and the gap in spatial feature representations. In this paper, we introduce PanoWan to effectively lift pre-trained text-to-video models to the panoramic domain, equipped with minimal modules. PanoWan employs latitude-aware sampling to avoid latitudinal distortion, while its rotated semantic denoising and padded pixel-wise decoding ensure seamless transitions at longitude boundaries. To provide sufficient panoramic videos for learning these lifted representations, we contribute PanoVid, a high-quality panoramic video dataset with captions and diverse scenarios. Consequently, PanoWan achieves state-of-the-art performance in panoramic video generation and demonstrates robustness for zero-shot downstream tasks.

Pano Wan: Lifting Diffusion Video Generation Models to 360 with LaiitudelLogiiude-awarr Mechanisms
Yifei X2a Shuchen Weng" Siqi Yang Jingqi Liu
Chengxuan Zhu
Minggui Tengla Zijian Jia Han Jiang' Boxin Shi
State Key Lab of Multimedia Info. Processing, School Computer Science, Peking University
Nat'l Eng. Research Ctr. of Visual Tech., School of Computer Science, Peking University
OpenBayee Information Technology Co., Ltd. "Beijing Academy of Artificial Intelligence IInstitute for Artificial Intelligence, Peking University Nat'l Key Lab of General AI, School of Intelligence Science and Technology, Peking University School of Artificial Intelligence, Beijing University of Posts and Telecommunications fyfxia, shuchenwegg, yousiki, petkereeeeeeeuuuu mingguinnneeneeeeeeee teng, Spiuo edu. cn liujingqi @stu. pku. edu. jiazijiantin edu. hahn@openbayes.cm com
Abstract
Panoramic video generation enables immersive 360 content creation, valuable in applications that demand scene-consistent world exploration. However, exist- ing panoramic video generation models struggle to leverage pre-trained genera- tive priors from conventional text-to-video models for high-quality and diverse panoramic videos generation, due limited dataset scale and the gap in spatial feature representations. In this paper, we introduce Pano Wan to effectively lift pre-trained text-to-video models the panoramic domain, equipped with minimal modules. Pano Wan employs latitude-aware sampling to avoid latitudinal distor- tion, while its rotated semantic denoising and padded pixel-wise decoding ensure seamless transitions at longitude boundaries. To provide sufficient panoramic videos for learning these lifted representations, we contribute PANOVID, a high- quality panoramic video dataset with captions and diverse scenarios. Consequently, PanoWai achieves sfate-of-the--e and demonstrates robustness for zero-shot downstream tasks. Our project page is available hhtpp:::/// :/ttt:////ppooan.n variantconst. com
performance in panoramic video generation
1 Introduction
Text-based panoramic video generation aims to produce a complete 360 view, ensuring coherent spatial and visual relationships between elements within the scene. Such inherent property highly valuable for conventional VR content, the construction of interactive game worlds [35] and the simulation of environments for embodied AI [88
The remarkable capabilities of conventional text-to-viee video models 28 motivate rescarchers to leverage their generative priors panoramic video generation. One intuitive strategy generates local perspective conventional videos and integrates them during inference. While these training- free methods 212 [2] entirely preserve generative priors, they sacrifice overall consistency they struggle to establish cross-view long-range dependencies. Alternatively, fine-tuning conventional text-to-video models [32 4] also faces challenges. On hand, existing panoramic video datasets are limited in scope and scale compared to conventional ones. On the other hand, the gap in spatial
"qqual contribution.
t Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Text-to-video generation
black hyper-car speeds through cyberpunk highway.
Cowboys ride through sunser-lir western town, visitors explore old streets.
Medieval event with knights jousting, crowds cheering, camps bustling lively.
Long video
Super-resolution
Low resolution
High resolution
Sunset at beach.
Lab with researchers and equipment.
Semantic editing
Video outpainting
Impainted Original
Outpainted
Oiigina
Change the color of the train to red.
Colorful hot air balloons.
Figure PanoWan is text-based panoramic video generation framework. lifts pre-trained generative priors from a conventional text-to-video model to the panorama, and enables generating diverse scenarios for long videos. Equipped with training-free techniques, Pano Wan supports zero- shot editing of panoramic videos, including super-resolution, semantic editing, and video outpainting.
representations (e.g., latitudinal distortions and seam longitudes) between panoramic and conventional videos potentially hinder the effective prior leverage from pre-trained conventional models.
In this paper, we pursue a boining-bbssd approach to overcome current bottlenecks. We propose PanoWan framework for Panoramic video generation based Wan 2.1 B3]. Equipped with minimal modules, PanoWan effectively lifts generative priors from pre-trained conventional text-to- video model to the panorama. To bridge the gap in spatial feature representations between panoramic and conventional videos, we design latitude-aware sampling to address latitudinal distortions caused by equireetangular projection. Since text-to-video models lack continuity awareness for left and right boundaries, achieve scamless longitude transitions using the rotated semantic denoising to address semantic inconsistency and the padded pixel-wise decoding to resolve pixel-wise disharmony.
As learning to lift spatial representations from conventional videos to the panorama requires large- scale data, we further introduce PANOVID. This Panoramic Video dataset offers diverse scenarios (e.g., landscape, streetscape, and humanscape), includes over 13K captioned video clips totaling 944 hours, and features data processing tailored for panoramic video generation. shown in Fig. [ our framework produces panoramic videos from text descriptions, enabling long video generation. Additionally, enables training-free editing of user-provided panoramic videos, including super- resolution, semantic inpainting, and video outpainting. Extensive experiments demonstrate that

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PanoWan achieves ttate-of-the art panoramic video generation performance across seven metrics, alongside robust zero-shot capabilities for various downstream tasks.
Our contributions can be summarized as follows:
We contribute PANOVID, a large-scale and high-quality panoramic video dataset with captioned video clips, tailored for text-based panoramic video generation.
We propose PanoWan, lifting generative priors from pre-trained model to show satte-of-theaan performance on panoramic video generation and robustness for downstream tasks.
We integrate the latitude-aware sampling. rotated semantic denoising, and padded pixel-wise decoding bridge the spatial difference between panoramic and conventional videos.
Related Works
2.1 Video Diffusion Models
Diffusion models have demonstrated impressive results in image generation, but extending them to videos introduces additional challenges (e.g.. temporal consistency and computational efficiency). Early models adapt image diffusion models with cascaded architecture [0]] temporal layers [25), and attention mechanisms 3 To further improve efficiency, LVDD 9
introduces a lightweight latent video diffusion model with a 3D latent space and hierarchical structure for long video generation. Meanwhile, SVD [4] improves video diffusion using large and high-quality datasets. Recent Diffusion Transformers (DiTs) [22] effectively model complex spatio-temporal dynamics for video generation, operating on latent space compressed by 3D VAE [39] After that, more large-scale models (e.g.. Hunyuan Video [1]. Cog VideoX [38]. and Seaweed [24] emerge and demonstrate the benefits of
scaling both model and data size. This motivates to adopt Wan 2.1 [33] as the backbone to leverage
its strong generative priors and temporal modeling capabilities for panoramic video generation.
2.2 Panoramic Video Generation
the pioneer to introduce stable video generation techniques to panoramic video generation by proposing 360-Adapter and set of 360 enhance- ment techniques. Training-free free methods (e.g., DynamicScaler [12] and SphereDiff (1 create panoramic videos by generating local patches and then composing them together into complete panorama, which inherently break global consistency. With the development of video diffusion models, VideoPanda (36) augments diffusion models with multi-view attention. PanoDiT 41 uses DiT backbone with global-tempon attention and panoramic-specifie losses for coherent long-range generation. Despite these ad advancements, existing methods still suffer from observable latitude dis- tortions and issues with seam-free longitude transitions. In this work, we introduce PanoWan, a framework that addresses these challenges by lifting generative priors from pre-trained text-to-video models to panorama.
Text conditioned generation. 360DVD 32
Image or video conditioned generation. Imagine360 [26] adopts antipodal-aware motion modeling to convert perspective videos to panoramic views. Building on static panoramas, 4K4DGen [16] lifts them into dynamic 4D scenes via spatial-temporal denoising. HoloTime [42 further leverages Gaus- sian splatting and a two-stage diffusion process for high-fidelity 4D reconstruction. VidPanos 20) treats panorama generation
space-time outpainting task from panning video inputs, while Argus [19] integrates motion and geometry cues for enhanced video-to-360" synthesis. These explo- rations highlight trend toward unifying spatial, temporal, and geometric reasoning. We demonstrate that Pano Wan possesses these capabilities, with robust zero-shot capabilities for downstream tasks.
PANOVID Dataset
The absence of paired datasets has long been regarded as one of the primary barriers to advancing the performance of panoramic video generation models [32]. Existing text to panoramic video generation methods 322 36 41
mainly rely on WEB360 dataset 32 which contains only 21 video clips of
10 seconds each. Although Argus [99 filters out over 283K video clips from the 360- 1M dataset 22] is not built for the text-based panoramic video generation task, providing no paired captions, and showing significant distribution bias for the scenario semantics.

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To address these limitations, we present PANOVID. a large-scale and high-quality dataset with diverse scenarios and balanced semantics, tailored for text-based panoramic video generation. Our data collection process begins by aggregating videos from existing panoramic sources, including 360- 1M [29]. 360+x [5], Imagine360 [26]. WEB360 62), Panonut360 [37], the Miraikan 360-degree
Video Dataset D. and a public dataset of immersive VR videos [15]. These sources cover both large-scale web collections (with rich but noisy YouTube content) and more curated institutional datasets (with higher quality but limited size).
Vision-language-basee filtering pipeline. To transform these heterogeneous sources into high- quality paired dataset, we design a scalable sive-stage filtering pipeline guided by laiinnl model. (i) Initial filtirnn by popularity: We first filter large-scale collections such as 360- 1M [29], retaining only videos with at least 1000 views to discard low-quality or trivial content. (ii) Shor segmentation: Each raw video is segmented into 10-second continuous clips using PySceneDetect, ensuring scene consistency and temporal coherence. (iii) Fision-languag annotation: We employ Qwen-2.5-V6 VI. 2 to process each clip, generating descriptive caption and predicting the associated POI (Point-of-Interee)
category in structured JSON format. Notably, we retain only clips that the model identifies as true ERP (equirectangular projection) panoramic videos. (iv) Motion score filtering: Following 77. we compute normalized optical-flow magnitude and remove clips with motion score below 0.4 to exclude static or nearly still scenes. (v) Aesthetic score filtering: Finally, utilize Q-Align 34 to evaluate the aesthetic quality of each frame, discarding clips with mean aesthetic score below 3.0. This ensures that the dataset maintains a consistent level of visual quality. This hierarchical filtering process effectively removes noise while preserving diversity and realism, yielding panoramic video clips that are semantically meaningful, visually appealing, and motion-rich. It also enables large-scale automatic caption generation without manual annotation cost.
Semantic balancing. We observe that the automatically annotated POI categories exhibit strong long-tail distribution: natural scenes such as Mountains and Parks dominate, while indoor and human-centric environments (e.g., Libraries, Shops, Theaters) are underrepresented. To alleviate this imbalance and enhance the generalization of models trained on PANOVID, we cap the maximum number of clips per POI category to 200, selecting the highest-ranking clips based on a combination of aesthetic and motion scores. All clips from underrepresenneed categories are fully retained. This strategy prevents overfitting to dominant scene types and ensures a diverse representation across environments.
Dataset statistics and characteristics. After the filtering, balancing, and a final deduplication step based on caption similarity, PANOVID comprises over 13K high-quality panoramic video clips, totaling approximately 944 hours of content. Compared with prior datasets, PANOVID offers not only substantially larger scale but also structured text-video pairs with fine-grained POI categories and balanced semantic coverage. thereby provides a robust foundation for training and evaluating text-conditioned panoramic video generation models.
Method
Panoramic videos have a different spatial feature representation compared to conventional ones. Inspired by GEN3C (23). we effectively preserve the generative prior of pre-trained models by equipping minimal modules and fine-tuning small subset of parameters via LoRA [Л.. We firstly introduce our video diffusion backbone and formulate the spherical coordinate mapping (Sec. (4.1) Next, we propose the latitude-aware sampling to avoid latitude distortion, along with its corresponding analysis (See. 4.2p. Finally, we present the rotated semantic denoising and the padded pixel-wise decoding to achieve the seamless longitude transitions (Sec.
4.1 Preliminaries
Video diffusion models. We employ Wan 2.1 33 as the video generation backbone, with spatial- temporal Variational AutoEncoders (VAEs) to map high-dimensional videos into compact latent codes. The flow matching framework 17 is used to model unified denoising diffusion process. Specifically, given a clear video VAE encoder EX-) first projects the video into the latent space 21 = F(r)). During training, a noise 2g A((0, T) is sampled, and an intermediate latent code tzj (1- t) is constructed by linearly interpolating between 21 and zo at timestep t E [0, 1].

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The training goal is to predict the ground truth velocity u, d /tt = and the loss function formulated as:
C Ezo, / Cxx, 1; - Ut ||?
where Coa is the text embedding, 0 is the parameters of the prediction model, and w(=e, Cua, 1;0) is the predicted velocity of the model.
Spherical coordinate mapping. Panorama captures a 360 view, inherently representing signals in spherical coordinates (f,,0). 0). To leverage generative priors from conventional images and videos that operate in Cartesian coordinates (a, V), we employ the equirectangular projection (ERP) PERP to map between these coordinate systems for panoramic videos:
PERP: [2/]] x
[R] + [0, 2T
π T 21 (r. - (2,0) = 2R "1 x
2' 2
2x +
2y + +1--- R 7).
(2)
2R
where R is the radius of the sphere. and 0 are longitude and latitude respectively. While ERP enables the direct application pre-trained VAEs to encode panoramic videos into latent codes for diffusion processes, it introduces extreme horizontal stretching in polar regions. This horizontal stretching phenomenon arises from the altered representation of distances during projection, and recognized by changes in horizontal signal frequency. Let d& and da represent the infinitesimal are lengths along lines of constant latitude and longitude, respectively. They are formulated as:
d 2R arcsin(cos@) A. sin di Ros 0 dip, dsg R d0,
where 0 is the latitude. We further consider the spherical frequency fagh (cycles per unit physical distance) and the Cartesian frequency for (cycles per pixel in the image). Assuming that warping preserves content, their relationship frequency is scaled by the change in distance:
dsg
ds
fear.y faph (pp) do
= Rfsph,0(()),
fear = faph.
= Righ (0) cos
(4)
dip
Consequently, in polar regions (10) == z. namely 0 or R), the horizontal frequency in the Cartesian coordinate becomes near-zero (y) s 0). Such distortion in the horizontal frequency distribution significantly degrades the effectiveness of transferring priors.
4.2 Latitude-Aaare Mechanisms
Latitude-aware sampling. Conventional text-to-video models typically assume independent and identically distributed (i.i.d.) Gaussian noise vectors for each Cartesian coordinate (z, y). To avoid latitudinal distortion in polar regions of ERP, propose the latitude-aware sampling better align the initial noise with the spherical frequency distribution for panoramic video generation. As illustrated in the top-left of Fig. 2 our latitude-aware sampling remaps the horizontal sampling coordinates based on latitude to preserve frequency consistency across the sphere. Specifically, after initializing the latent map with i.i.d. Gaussian noise vectors, calculate the sampling noise by remapping the horizontal sampling coordinate z based on the latitude corresponding to row y, and then applying the interpolation:
P'(x, y) = Interp (R (x R) cos(
2y +
TT), v)
(5)
2R
where P'(x, y) is the interpolated noise vector at coordinate (x. Interp(:) is formulated as the interpolation function for normalization:
Interp, v) sgn(BI(P,, B BI(P", 2,
(6)
5

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x
Padded decoding
denolsing
VAE decoder
Latludo aware sampling
Initial noise
Spheric noise
+ VAE encoder
DIT
Remove padding
LoRA fine-tuning + Fixed parameters
Input video
Text prompt
Text encoder
Latent diffusion
Reconstructed video
Figure 2: The pipeline of our proposed PanoWan, aware of spherical coordinates. To avoid latitudinal distortion, initial random Gaussian noise is remapped to align with the spherical frequency distribution using the latitude-aware sampling (Sec. 4.2) Next, this remapped noise serves as the latent code within the VAE-encoded latent space. A DiT-based denoising network then iteratively refines this latent representation, where rotated denoising is applied by rolling the latent grid to ensure semantic consistency across longitudinal boundaries. After that, padded pixel-wise decoding provides the VAE decoder with extended context, enabling the mapping of the denoised latent code back into seamless panoramic videos (Sec. 4.3). The DiT backbone within Pano Wan efficiently fine-tuned using LoRA, where most parameters of the pre-trained text-to-video model remain frozen to preserve its strong generative priors.
where sgn(-) is the sign function, and (I(P, v) the standard bilinear interpolation for vector P at coordinate (x, y). Consequently, the resulting noise vectors sampled by our strategy preserve E[P"(a: (z. y)] and Var (x, y)] approaching the distribution on which the diffusion models are pre-trained. The proof is given in the supplementary materials.
Frequency domain analysis. We aim to prove that the horizontal frequency properties of the proposed sampling correctly represent the inherent properties the spherical coordinate, following the methodology of 1-D Discrete Fourier Transform (DFT). Denote the maximal frequency of the original signal in the spherical space as fmax. and the maximal frequency the Cartesian coordinates the latitude of 0 is determined by:
max Fear. (v) max Reos 0 fsph. (f) S Rmxx:
where the equation only holds when 0 = O, namely on the equator. For the proposed design, it guarantees max fear,e 2R along the equator as cos = 1 and the original pixels along P(, R-1 is taken. Therefore, the maximal spherical frequency is fmax = 2. According to Eq. (5), the warped results only depends on the values between (R cos(@). y) and (R + (R 1) cos( y) in the original Cartesian grid P. According to DFT, the support of the spectrum is reduced to:
[ (os)) se 2R cos = fmax R(0) cos = max (ar,((y), 4O E
π π
(8)
2
This matches the inherent property of the panorama in the frequency domain in every latitude.
4.3 Longitude Continuity Mechanisms
Seamless longitude transitions. Pre-trained conventional text-to-video models lack continuity awareness required between the columns of left and right boundaries. Consequently, applying their generative priors directly for panoramic video generation leads to seam artifacts, resulting in an observable transition where the easternmost and westernmost longitudes meet. To achieve seamless longitude transitions, we recognize that these artifacts arise from both semantic inconsistency and pixel-wise disharmony. This motivates us to propose the rotated denoising and the padded decoding to significantly remove the artifacts.

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Rotated semantic denoising. The video generation backbone inherently introduces semantic in- consistency at each denoising step. Since the pre-trained generative priors lack the continuity awareness, the semantic in leftmost and rightmost longitudes is typically inconsistent, which are further accumulated during the iterative denoising steps and finally produce an obvious transition.
Our proposed rotated semantic denoising aims to spread the transition error evenly to different longitudes. Let R.. (-) be the circular-shift operator and W denote the width of the latent code. As shown in Fig. 2 we horizontally roll the latent code Ze by {st- t mod W } columns at denoising step & and then undo the shift:
Z++1 = R.-s, (R (4))):
(9)
where (o(-) is the noise predictor. As a result, the inherent accumulative error for horizontal coordinate x after T denoising steps is:
T ET(Φ) - Σ ((x (I + st) mod W), 1=1
(10)
where E-(-) is prediction error for the transition and step f, which would concentrate at a fixed seam no rotation are applied. Due to the rotation strategy, this error at physical coordinate x at step l is determined by the logical position {(x + 8x) mod W). Over T steps, these logical coordinates {(x + 8t) mod W) W) ideally approach uniform permutation for all longitudes. This effectively suppresses seam artifacts by factor approaching 1/W.
Padded pixel-wise decoding. When decoding latent codes back to the pixel space, the pre-trained VAE decoder D often introduces pixel-wise inconsistencies, as it is trained on conventional videos and lacks awareness of the spatial continuity required across the left-right seam of panoramic videos [99] Inspired by previous works [30, 40) we present the padded pixel-wise decoding. Let Zo be the denoised latent code. We first create a padded latent code Z = P. (Za). where P-(-) is a circular padding operator that extends Zo by columns of context on the side, and the content at horizontal coordinate x is {Ir mod W in Zo. Finally, we center crop the decoded panoramic videos after the decoding V Crop ((o)))
illustrated in Fig. 2 This approach ensures that pixels near the original seam boundaries are decoded with r columns of horizontal panoramic context. Consequently, the VAE decoder can effectively leverage its generative priors learned from conventional videos to avoid the seam artifacts.
5
Experiments
5.1 Training Details
33 as the video generation backbone. We train PanoWan at resolution of 448 x 896, closely matching the pre-trained resolution of this backbone model. For parameter-efficient training. LoRA [I] with a rank of 64 is applied to the query, key, value, and output projections of the attention mechanisms, well as to the feed-forward networks. The model is trained for 200K iterations on our contributed PANOVID dataset. The training process employs the [3] with a learning rate of 10-4 4 and a batch size of 8. Training conducted
PanoWan is built on Wan 2VV
Adam W optimizer on 8 NVIDIA H100 GPUs for approximately 18 hours. During each iteration, clips of 81 consecutive frames are randomly sampled from the videos. Consequently, only 21.9M parameters are adjusted, constituting approximately 1.6% of the base model's 's total parameters.
5.2 Panoramic Video Evaluation Metrics
Existing panoramic video generation methods either directly apply conventional video evaluation metrics [2 41 or rely on subjective user preferences [32], lacking metrics that comprehensively assess both perceptual quality and spherical consistency critical for panoramic video evaluation. This motivates us to adapt general video quality metrics for panoramic videos and to introduce additional panorama-specific metrics for structural properties of 360 content.
General metrics. We apply Frechét Video Distance (FVD) 27 to evaluate overall video quality and VideoCLIP-XL 31 to assess text-video alignment. Following DynamicScaler [2], we also
7

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Table 1: Quantitative comparison results of Pan and previous text-based panoramic video generation models. 1 (L) means higher (lower) is better. Throughout the paper, best performances are highlighted in bold.
Method
General Metries
Panoramic Metrics
VideoCLIP- XL f
Image Quality
End Continuity
Motion Pattern
Scene Richness
FVD 4
360DVD 32
1750.36
20.27
0.7054
0.0323
5.8%
6.6%
DynamicScaler
12
2146.04
21.13
0.7188
0.0339
4.0%
2.6%
Ours (Wo LAS)
1520.69
21.20
0.7205
0.0278
16.2%
19.4%
Ours (Wo RSD)
1302.48
21.76
0.7243
0.0327
15.6%
18.8%
Ours (Wo PPD)
1294.03
21.81
0.7239
0.0294
22.0%
17.4%
Ours (full)
1281.21
21.86
0.7249
0.0270
36.4%
35.2%
calculate specific metrics for image quality. To adapt these general metrics for panoramic videos, we project each video onto cube map and compute metric scores separately each of the six faces. The final reported score for a video is weighted average:
f(v)
-= La..D100 (Pp.)(P(())). FEF
(11)
where F denotes the set of cube map faces, Py(e) is the projection of video v onto face f E F,, 4 is the metric function, and a is the weight assigned to face f. Following OmniFID [6], we assign and C'side T2 for each of the four lateral faces.
weights Chop Obootoom
Panoramic metrics. Following previous works 32 richness with user preferences. We additionally introduce quantitative metric for evaluating the end continuity of generated panoramic videos, tailored to capture artifacts across longitude boundaries. Specifically, this metric computes the mean absolute pixel difference across the left and right boundaries, directly capturing discontinuities the longitude seam.
we evaluate motion patterns and scene
5.3 Comparison with State-of-the-art Methods
We evaluate PanoWan against existing text-based panoramic video generation methods, including 360DVD [32 and DynamicScaler [12], on the PANOVID test split containing 67 non-overlapping clips. Quantitatively, as shown in Tab. PanoWan achieves state-of-the-an performance across both general and panoramic metrics (detailed Sec. Sec. Qualitatively, we present visual results to highlight our advantages. For instance, DynamicScaler [12] falls short in complex scenarios (Fig. 3 first sample), and 360DVD [32) exhibits notable distortion in polar regions (Fig. 3 second sample). In contrast, PanoWan Wan effeettively maintains global consistency and visual coherence, achieving superior performance in generating high-fidelity panoramic videos.
Following 360DVD 332, we also conducted numan--reeereee study to complement automatic metries. 25 participants compared videos generated from 50 random test prompts among PanoWan, 360DVD (22, and DynamicScaler [12]. PanoWan was preferred in 71.36% of cases, versus 16.77 72% and 11.92% for 360DVD [32] and DynamieScaler [12), respectively. These results confirm that the perceptual improvements observed quantitatively are also recognized subjectively.
5.4 Ablation Studies
We conduct ablation studies to validate the effectiveness of proposed modules in PanoWan: latitude- aware sampling (LAS), rotated semantic denoising (RSD), and padded pixel-wise decoding (PPD).
Quantitative results. As shown Tab. removing LAS primarily affects general metrics- FVD increases from 1281.21 to 1520.69, and VideoCLiP-XX drops from 21.86 to 21.20 220-iiniia the model struggles to learn panoramic features in high-latitude regions without frequency-aligned noise initialization. In contrast, removing RSD or PPD mainly degrades panoramic metrics (e.g., end continuity increases from 0.0270 to 0.0327 and 0.0294, respectively), confirming their roles in achieving seamless longitude transitions.

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360D DynamicScal
Ours
Wide-angle panoranic interior capturing an energetic hot pot restaurant bustling with diners enthssasticaly cooking ingredients in bubbling pots. Colorful plates arranged invitiggy, steam rising, spirited con conversations filling warm, inviting atmosphere emphasizing community and culinary enjoyment.
Ours 360DVD DynamicScales
Expunsive panoramic view capturing a vibrant ski resort nestled among towering snowy mountuins, skiers gracefully descend pristine slopes amid cozy alpine chalets. Chairlifts glide leisurely under crisp biue skies while visitors gather around lively outdoor eafes, soaking the sunny winter scenery.
Figure 3: Visual comparison results with existing text-based panoramic video generation methods.
Qualitative results. We further provide qualitative evaluation in Fig. 4 When generating high- latitude elements like LED panels (which should appear straight in perspective views but are inherently distorted in the equirectangular projection), PanoWan without LAS fails render them with the correct geometric appearance. When RSD is discarded, semantic inconsistencies become apparent at the longitude seam, due the lack of mechanism for continuity awareness and the error accumulation during the denoising process. When PPD is removed, observable seam artifacts occur because conventional VAE decoder introduces pixel-wise inconsistencies boundaries. Consequently, the full PanoWan model with all modules enabled achieves the best performance.
5.5 Application
As text-based panoramic video model, PanoWan shows robust zero-shot capabilities across wide range of downstream tasks. We present representative examples in Fig. T and additional examples in supplementary materials due the space limitation.
Long video generation. To generate panoramic videos longer than the model's native temporal context, we adopt sliding-window inference strategy in the latent space. At each denoising step, the latent code is partitioned into temporally overlapping chunks, where each chunk is conditioned on a corresponding segment of text prompts expanded by LLM. Adjacent chunks share overlapping frames that serve as temporal context, and their outputs are fused through a linear blending function on the overlapping regions.
Super-resolution for panoramic videos. To generate high-resolution panoramic videos from low- resolution ones, first encode each low-resolution video into its corresponding latent code. After

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W/ RSD
W/ LAS
W/ PPD
Equirectangular
Perspective
W/o LAS
Full model
Equirectangular
Perspective
(a) Visual evaluation of LAS
Visual evaluation of RSD and PPD
Figure 4: Qualitative evaluation of proposed latitude/longitude-avaa mechanisms. (a) With the proposed Latetude-Aware Sampling (LAS), PanoWa ensures that content generated at high latitudes exhibits accurate geometry when presented in perspective view. (b) By combining Rotated Semantic Denoising (RSD) and Padded Pixel-wise Decoding (PPD), Pano Wan achieves seamless longitude transitions. For visualization, videos are rolled 180 center the seam.
injected noise, the latent code is denoised based on user-provided text descriptions, producing results with structural consistency and visual fidelity across the spherical representation.
Inpainting for semantic editing. Given panoramic video, we identify and mask regions for modification. Next, we apply the denoising process to these regions, guided by user serr-poovdee text descriptions. Leveraging its understanding of spherical representations, the inpainted content naturally exhibits the properties of ERP projection.
Outpainting for conventional videos. Similar the inpainting process, first map the conventional video to the latent code and then mask the surrounding unseen panoramic regions. With user-provide text descriptions, we denoise the masked regions generate corresponding content. Our pre-trained model maintains the spatial and temporal consistency for generated panoramic videos.
6 Conclusion
We present PanoWan, a text-based panoramic video generation framework that effectively lifts pre-trained diffusion model to the panorama. By integrating latitude-aware sampling, PanoWan addresses latitudinal distortions caused by equirectangular projection. Equipped with the rotated semantic denoising and the padded pixel-wise decoding, PanoWan achieves the seamless longitude transitions. To provide large-scale data for lifting representations from conventional videos to the panorama, contribute the PANOVID dataset, offering high-quality and semantically rich 360 video data with annotated text descriptions. Extensive experiments demonstrate that PanoWan achieves Mfte-of-theear performance on text-based panoramic video generation and strong generalization across diverse zero-shot downstream tasks.
Limitation. While PanoWan benefits from the strong priors of its pretrained text-to-video models, is also inherits a common challenge: the content forgetting problem often seen such models. This issue is particularly evident when generating long videos due limited temporal memory. We believe this challenge can substantially alleviated through future advancements in memory-aware generation techniques (e.g., video caching mechanisms).
Acknowledgement. This work is supported by National Natural Science Foundation of China under Grant 63.62136001. Authors thank openbayes.com for providing computing resources.
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References
[1] Miraikan 360-degree video dataset. Accessibiiyy.aa/aaaae:330/
attps://www.miraiiaa..PP eew.ja/////eeearcch
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Please refer to L.LM htteps////errpppeccc///oeeeeeeep for what should or should not be described.
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Reviews:
[{"review_id": "vXqqfAlER7", "reviewer": "Reviewer_R4PZ", "summary": "This paper introduces PanoWan, a panoramic video generation framework that lifts conventional text-to-video diffusion models (specifically Wan 2.1) to the 360° domain. It addresses two major spatial challenges in this domain: latitudinal distortion (caused by equirectangular projection) and longitudinal discontinuity (seams at panorama boundaries). The method includes three main components: Latitude-Aware Sampling to address polar distortion, Rotated Semantic Denoising to reduce semantic inconsistencies at the longitudinal seam, and Padded Pixel-wise Decoding to handle pixel-level discontinuities. In addition, the paper contributes PANOVID, a new panoramic video dataset with >13K captioned clips, and evaluates the method across multiple metrics, showing strong gains over prior work (360DVD, DynamicScaler, etc.) in both general and panoramic-specific metrics.", "questions": "- How sensitive is PanoWan to the choice of base model (e.g., Wan 2.1)? Would a smaller or different T2V backbone yield similar gains when equipped with LAS/RSD/PPD? This could influence how broadly applicable the method is.\n- How is temporal consistency affected by RSD? Since the latent grid is rotated every timestep, could this affect temporal coherence negatively in subtle ways? Some analysis or visualization of temporal smoothness would be helpful.\n- What is the annotation quality of PANOVID? The dataset uses Qwen-VL for captioning and POI detection. Has human verification been done to assess caption accuracy or bias?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The proposed LAS, RSD, and PPD models are modular, intuitive, and easily integrated into existing architectures without major overhead. Each proposed module is evaluated in isolation, demonstrating its distinct contribution to the final performance.\n- The method achieves solid gains across both general (FVD, VideoCLIP-XL) and panoramic-specific metrics (end continuity, scene richness).\n- The newly-proposed PANOVID dataset is a valuable contribution to the community given the previous scarcity of large-scale captioned 360° video datasets.\n\nWeaknesses\n- While 360DVD and DynamicScaler are included in comparisons, more recent and competitive baselines like PanoDiT or VideoPanda, both specifically designed for panoramic video generation, are only briefly mentioned and not fully compared in experiments.\n- Though quantitative metrics are provided, there is no human study or perceptual evaluation, which is especially important for tasks like panoramic video generation where spatial consistency is nuanced and hard to fully capture with automated metrics.\n- PANOVID captions are generated by Qwen-VL with no clear human curation. The lack of quality assurance or discussion of hallucination/bias in these captions could impact the reliability of both training and evaluation.", "was_revised": true, "final_justification": "As discussed in the threads, I will keep my original rating unchanged at this point. I also appreciate the authors' effort in adding a summary of the discussion phase.", "rebuttal": "We thank the reviewers for their insightful and constructive feedback.\n\nIn accordance with the rebuttal policy, we are unable to include new qualitative results (e.g., generated videos) or external links. We assure the reviewers that, should the paper be accepted, the camera-ready version will be thoroughly updated to include all additional experiments and qualitative results discussed in this rebuttal.\n\nWe now address the specific points raised by the reviewer.\n\n**W1: Comparison with PanoDiT and VideoPanda**\n\nWe thank the reviewer for the insightful suggestion regarding more recent baselines. However, at the time of our submission, both PanoDiT and VideoPanda are not open-sourced, making a direct and reproducible comparison infeasible. Therefore, we chose the publicly available 360DVD and DynamicScaler, which represent two distinct and representative technical paradigms: the classic ERP-based generation (360DVD) and training-free diffusion adaptation (DynamicScaler). We are confident this comparison framework is fair and effectively demonstrates the advantages of our method.\n\n**W2: Human evaluation**\n\nFollowing 360DVD, we have provided initial subjective evaluations in our main paper regarding motion pattern and scene richness (Lines 223-224). To further address the reviewer's concerns, we conduct an additional user study as requested.\n\nIn this study, we present videos generated by our method and all baselines to participants and ask them to choose the one they prefer. We randomly select 50 text descriptions from the test set, and the evaluations are conducted with 25 volunteers. The table below shows the percentage of times each method is chosen as the winner. As the results show, our method achieves the highest score.\n\n| Method | 360DVD | DynamicScalar | PanoWan |\n| :---: | :---: | :---: | :---: | \n| Preference | 16.72%  | 11.92% | **71.36%** |\n\n**W3: Annotation quality**\n\nAs we illustrate in Sec. 3 (Lines 96-98) and supplementary materials (Sec. 9), we use Qwen-VL to generate text descriptions and Point-of-Interest (POI) categories for each video, and also apply a series of data filtering and post-processing steps.\n\nTo further validate the sample quality of our dataset, we conduct an additional human evaluation. We randomly sample 1000 samples from the dataset and ask 10 volunteers to classify the alignment of each one into three categories: \"Highly aligned\", \"Partially aligned\", or \"Misaligned\".\n\nAs the table below shows, over 94% of text description annotations and 90% of POI annotations are rated as \"Highly Aligned\", respectively. This result demonstrates that our data pipeline is robust and the generated annotations are reliable for both training and evaluation.\n\n| Label type | Highly aligned | Partially aligned | Misaligned |\n| :---: | :---: | :---: | :---: | \n| Text description | 94.96%  | 4.35% | 0.69% |\n| POI category | 90.17% | 7.69% | 2.14% |\n\n**Q1: Base model**\n\nOur framework is indeed not limited to Wan 2.1. The core modules (LAS, RSD, and PPD) are independent of the base model and they are designed to address fundamental challenges in panoramic generation, rather than being specific to any single architecture:\n\n- LAS adapts noise initialization to spherical geometry.\n\n- RSD and PPD restructure the denoising and decoding process to improve spatial consistency and perceptual fidelity.\n\nTo empirically validate this generalizability, we conduct an additional experiment as suggested. We integrate our modules into the T2V backbone used by 360DVD and retrain them under the same settings. The results in the table below demonstrate that our modules provide a clear improvement in edge continuity when applied to the 360DVD backbone. Notably, we focus here on objective metrics, while \"motion pattern\" and \"scene richness\" are subjective and require a separate user study.\n\n| Method | FVD ↓ | VideoCLIP-XL ↑ | Image quality ↑ | End continuity ↓ |\n| :---: | :---: | :---: | :---: | :---: |\n| 360DVD (vanilla) | 1750.36  | 20.27 | 0.7054 | 0.0323 |\n| 360DVD + LAS/RSD/PPD | **1738.77** | **20.49** | **0.7030** | **0.0280** |\n\n**Q2: Temporal consistency of RSD**\n\nWe clarify that RSD does not harm temporal consistency. The core principle is that a horizontal roll in the ERP format is a spatially invariant operation, which does not change the visual content. Prior works like 360DVD validate this by using random roll as a standard data augmentation technique.\n\nOur RSD module leverages this property at each denoising step to solve the problem of persistent boundary artifacts. To demonstrate this, we conduct an additional study using fixed rotation angles during sampling. The results show that fixed angles of 0°, 180°, and 120° introduce 1, 2, and 3 visible seams, respectively.\n\nIn contrast, our RSD disperses these potential artifacts spatially. This prevents any single boundary from becoming a visible seam, thereby improving overall spatial and temporal coherence. We will include visualizations of this study in the final version for clarity.\n\n**Q3: Annotation quality**\n\nSee W3 please."}, {"review_id": "OMEjgOu8nD", "reviewer": "Reviewer_yfgp", "summary": "This paper tackles the text-to-360 video generation task. The framework employs Wan-2.1 as baseline and employs several necessary techniques for latitude sampling and longitude continuity. Experiments show high-quality generations and improved video quality metrics on general and panoramic domains.", "questions": "- Missing experiment details: How many videos are evaluated to obtain the metrics? What's the distribution of these test videos (in-the-wild  videos or generated videos?) More explanation of the evaluation setup is needed.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n+ The generated results look good with high-quality details and consistent polar patterns.\n+ Latitude-aware sampling is new, convincing, and validated effective by ablations.\n+ The framework shows zero-shot capabilities by investigating different types of downstream tasks, including important tasks such as video outpainting, video super-resolution, etc.\n\nWeaknesses:\n- Limited technical novelty: The method fine-tunes Wan 2.1 with LoRA and applies known techniques for 360° adaptation. Key components like Rotated Semantic Denoising and Padded Decoding have been introduced in 360DVD (Latent Rotation Mechanism, Circular Padding Mechanism) and PanFusion.\n- Missing comparison: As a new training dataset is introduced in the paper, it would be interesting to also train baselines (e.g., 360DVD) on the new dataset to see improvements.\n- Missing experiment details: How many videos are evaluated to obtain the metrics? What's the distribution of these test videos (in-the-wild  videos or generated videos?)", "was_revised": true, "final_justification": "The authors' response addresses most of my concerns. Therefore I raise my rating to borderline accept.", "rebuttal": "We thank the reviewers for their insightful and constructive feedback.\n\nIn accordance with the rebuttal policy, we are unable to include new qualitative results (e.g., generated videos) or external links. We assure the reviewers that, should the paper be accepted, the camera-ready version will be thoroughly updated to include all additional experiments and qualitative results discussed in this rebuttal.\n\nWe now address the specific points raised by the reviewer.\n\n**W1: Technical novelty**\n\nWe thank the reviewer for this critical feedback on our technical novelty. We would like to clarify our key contributions from both the latitude and longitude perspectives:\n\n1. Latitude-related novelty\n\n- 360DVD uses a latitude-aware loss to emphasize low- and mid-latitude regions. However, this approach essentially de-emphasizes the highly distorted polar regions rather than fundamentally solving the representation issue, which results in persistent distortion artifacts at the poles.\n\n- PanFusion introduces EPP spherical positional encoding, which modifies core attention layers. While effective in feature representation, this alters the architecture of the pre-trained conventional model, which can compromise its valuable generative priors and limit its ability to generate diverse content.\n\n- In contrast, our Latitude-aware Sampling (LAS) is a zero-parameter solution that requires no architectural changes. Instead, LAS introduces distortion-aware noise initialization directly into the diffusion process. This ensures spherical awareness while fully preserving the powerful generative priors of the pre-trained backbone.\n\n2. Longitude-related novelty\n\n- Although 360DVD proposes the latent rotation mechanism to address boundary discontinuity during denoising, we empirically observe that persistent visible seams remain. Our key insight is that this issue stems from two distinct stages: (1) the denoising process and (2) the VAE decoding process. While latent rotation helps with the first stage, it overlooks the second. As a result, standard VAEs trained on conventional videos with zero-padding re-introduce artifacts when decoding the latent into pixel space.\n\n- To resolve this, we first adapt the latent rotation mechanism and introduce it into the rectified flow of the Wan 2.1 backbone as Rotated Semantic Denoising (RSD). As a complement, we propose Padded Pixel-wise Decoding (PPD) to pad the latent with its own wrapped content before feeding it to the VAE, and then crop the final pixel output. This ensures the decoder never processes invalid boundary padding.\n\n- Our proposed PPD is fundamentally different from 360DVD's \"circular padding mechanism\", which modifies convolutional layers within the U-Net denoiser, thus only addressing the denoising stage. In contrast, our PPD specifically targets the VAE decoding stage. Notably, the overhead of PPD is minimal, as the padding width only needs to exceed the VAE's receptive field.\n\nSummary of novelty:\n\n- Our framework introduces LAS, RSD, and PPD to adapt conventional text-to-video models for panoramic video generation. Our approach is guided by the core principles of minimal architectural change and full preservation of the base model's generative priors. \n\n- We apologize for the confusion. We sincerely thank the reviewer for pushing us to clarify our work's novelty, and we will incorporate this detailed comparison into the final version.\n\n**W2: Re-training 360DVD on PanoVid**\n\nWe follow the reviewer's suggestion to retrain 360DVD on our full PanoVid dataset. As shown in the results below, 360DVD's performance improves, which validates the quality of our contributed dataset. Despite this improvement, our PanoWan still consistently outperforms the retrained 360DVD. Since DynamicScaler is a training-free method, this experiment is not applicable to it. Notably, we focus here on objective metrics, while \"motion pattern\" and \"scene richness\" are subjective and require a separate user study.\n\nWe will include these results in the final version.\n\n| Method | FVD ↓ | VideoCLIP-XL ↑ | Image Quality ↑ | End Continuity ↓ |\n| :---: | :---: | :---: | :---: | :---: | \n| 360DVD (vanilla) |1750.36 |\t20.27 | 0.7054 | 0.0323 |\n| 360DVD (retrained) | 1643.31 | 20.84 | 0.7129 | 0.0337 |\n| PanoWan | **1281.21** | **21.86** | **0.7249** | **0.0270** |\n\n**W3: Evaluation details**\n\nWe follow the reviewer's suggestion to clarify our evaluation details.\n\nAll quantitative results are computed on the PanoVid test set, which consists of 67 real-world video clips collected from various online platforms and public datasets. These clips have no overlap with the training data to ensure a fair evaluation. For each clip, we use its corresponding text description as input to generate video samples, and then compute all metrics on these generated videos.\n\nWe will incorporate these details in the final version to ensure the reproducibility and transparency.\n\n**Q1: Evaluation setup**\n\nSee W3 above."}, {"review_id": "lkVlT0NjvO", "reviewer": "Reviewer_uRF1", "summary": "The paper presents PanoWan, a novel framework that adapts a pre-trained text-to-video diffusion model (Wan2.1) for generating high-quality 360° panoramic videos. To address spatial distortion in polar regions, the authors introduce latitude-aware noise sampling, which aligns the noise initialization with spherical frequency distributions. Additionally, the framework employs rotated semantic denoising and padded pixel-wise decoding to ensure seamless transitions at longitudinal boundaries. The authors further contribute PanoVid, a large-scale, high-quality dataset of panoramic videos comprising 13K captioned clips, providing a valuable resource for future research in panoramic video generation.", "questions": "About the application of 'Long-video generation', could the authors provide more details regarding how the model handles long-context training and inference? Maintaining consistent quality over extended sequences is a known challenge—are there any specific mechanisms or design choices employed to mitigate quality degradation in long-video synthesis?\n\nWhen curating the dataset using a vision-language model (VLM), unstable outputs or partial model collapse can sometimes occur, especially in open-ended generation settings. Did the authors encounter any such failure cases during the annotation process? If so, what strategies or safeguards were implemented to address or filter out such noisy samples?", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\nThe curated dataset provides good contributions to the community, particularly for research on panoramic video generation.\nThe proposed latitude-aware sampling strategy introduces a novel perspective and demonstrates thoughtful designs.\nExtensive experiments using both general video metrics (FVD, VideoCLIP-XL) and panorama-specific metrics (e.g., end-to-end continuity) show the method’s superiority over existing baselines.\n\nWeakness:\nSome of the technical components, such as rotated semantic denoising and padded pixel-wise decoding, are not entirely novel—similar strategies have been explored previously in works like PanFusion [Zhang, Cheng, et al.]. \nThe proposed applications are mostly adopted from the features of the base model (Wan2.1).", "was_revised": true, "final_justification": "The author's rebuttal mostly solved my concerns. Overall, I think the generated video quality is at the SoTA level in this field. The experiments are comprehensive, and the applications are sufficient to demonstrate their capability. Therefore, I'll maintain my original score as BA.", "rebuttal": "We thank the reviewers for their insightful and constructive feedback.\n\nIn accordance with the rebuttal policy, we are unable to include new qualitative results (e.g., generated videos) or external links. We assure the reviewers that, should the paper be accepted, the camera-ready version will be thoroughly updated to include all additional experiments and qualitative results discussed in this rebuttal.\n\nWe now address the specific points raised by the reviewer.\n\n**W1: Technical novelty**\n\nWe thank the reviewer for this critical feedback on our technical novelty. We would like to clarify our key contributions from both the latitude and longitude perspectives:\n\n1. Latitude-related novelty\n\n- PanFusion introduces EPP spherical positional encoding, which modifies core attention layers. While effective in feature representation, this alters the architecture of the pre-trained conventional model, which can compromise its valuable generative priors and limit its ability to generate diverse content.\n\n- 360DVD uses a latitude-aware loss to emphasize low- and mid-latitude regions. However, this approach essentially de-emphasizes the highly distorted polar regions rather than fundamentally solving the representation issue, which results in persistent distortion artifacts at the poles.\n\n- In contrast, our Latitude-aware Sampling (LAS) is a zero-parameter solution that requires no architectural changes. Instead, LAS introduces distortion-aware noise initialization directly into the diffusion process. This ensures spherical awareness while fully preserving the powerful generative priors of the pre-trained backbone.\n\n2. Longitude-related novelty\n\n- Although previous work (e.g., 360DVD) proposes the latent rotation mechanism to address boundary discontinuity during denoising, we empirically observe that persistent visible seams remain. Our key insight is that this issue stems from two distinct stages: (1) the denoising process and (2) the VAE decoding process. While latent rotation helps with the first stage, it overlooks the second. As a result, standard VAEs trained on conventional videos with zero-padding re-introduce artifacts when decoding the latent into pixel space.\n\n- To resolve this, we first adapt the latent rotation mechanism and introduce it into the rectified flow of the Wan 2.1 backbone as Rotated Semantic Denoising (RSD). As a complement, we propose Padded Pixel-wise Decoding (PPD) to pad the latent with its own wrapped content before feeding it to the VAE, and then crop the final pixel output. This ensures the decoder never processes invalid boundary padding.\n\n- Our proposed PPD is fundamentally different from previous \"circular padding mechanism\" (from 360DVD), which modifies convolutional layers within the U-Net denoiser, thus only addressing the denoising stage. In contrast, our PPD specifically targets the VAE decoding stage. Notably, the overhead of PPD is minimal, as the padding width only needs to exceed the VAE's receptive field.\n\nSummary of novelty:\n\n- Our framework introduces LAS, RSD, and PPD to adapt conventional text-to-video models for panoramic video generation. Our approach is guided by the core principles of minimal architectural change and full preservation of the base model's generative priors. \n\n- We apologize for the confusion. We sincerely thank the reviewer for pushing us to clarify our work's novelty, and we will incorporate this detailed comparison into the final version.\n\n**W2: Downstream applications**\n\nWe thank the reviewer for this observation, which in fact highlights a central goal of our work.\n\nWe would like to clarify that our primary objective with PanoWan is to demonstrate that generative priors of pre-trained conventional text-to-video generation models can be effectively lifted to the panorama with minimal equipped modules (Line 30-32). The downstream applications presented in our paper (e.g., long-video generation, super-resolution, semantic editing, and video outpainting) are therefore intended as a direct validation of this principle. \n\nAs the results in Fig. 1 and Sec. 5.5 show, PanoWan effectively preserves the generalization ability of the backbone while enabling high-quality panoramic video generation. Notably, all of these experiments are performed in a zero-shot manner.\n\n**Q1: Long video generation**\n\nTo generate a video longer than the model's context length (e.g., 324 frames using a model trained for 81-frame segments), we adopt a sliding window approach during the inference process in latent space. The methodology is as follows:\n\n1. A full-length latent code (324 frames) is initialized with standard Gaussian noise. Concurrently, the user-provided text description is expanded by an LLM into a sequence of prompts ([prompt_1, prompt_2, ...]), each corresponding to a specific temporal segment.\n\n2. For each step during inference, we iterate through the full latent code using overlapping windows (e.g., an overlap of 40 frames). For each window, the corresponding latent segment is processed by the DiT backbone, conditioned on its corresponding prompt. To ensure temporal consistency, context frames from the previously processed adjacent window are used to guide the generation of the current segment. The output of each window is then fused back into the full-length latent code by applying a weighted average in the overlapping chunks, ensuring a seamless transition.\n\n3. After the inference phase, the resulting clean latent code is decoded by the VAE to produce the final long video.\n\n**Q2: Annotation reliability**\n\nWe agree that raw VLM outputs can be unstable in open-ended annotation tasks. In practice, we encounter occasional misclassification of non-panoramic videos and generated Points-of-Interest (POIs) that do not adhere to our predefined label set (e.g., case variations or synonyms).\n\nTo address this, we design a dedicated multi-step prompt strategy (detailed in the supplementary materials, Sec. 9) to first verify panoramic properties  (Line 83-84) and then extract POI information (Line 85-93). Furthermore, we employ the same VLM in a post-processing step to reformat the extracted information (Line 97-107), ensuring strict consistency with our target label set (Line 114-129).\n\nTo further validate the sample quality of our dataset, we conduct an additional human evaluation. We randomly sample 1000 samples from the dataset and ask 10 volunteers to classify the alignment of each one into three categories: \"Highly aligned\", \"Partially aligned\", or \"Misaligned\".\n\nAs the table below shows, over 94% of text description annotations and 90% of POI annotations are rated as \"Highly aligned\", respectively. This result demonstrates that our generated annotations are reliable.\n\n| Label Type | Highly aligned | Partially aligned | Misaligned |\n| :--- | :---: | :---: | :---: |\n| Text description | 94.96% | 4.35% | 0.69% |\n| POI Category |  90.17% | 7.69% | 2.14% |"}, {"review_id": "xbiSVVmHHu", "reviewer": "Reviewer_sK8Y", "summary": "This paper proposes PanoWan, a novel framework for panoramic video generation based on the Wan 2.1 model. First, the authors address the lack of high-quality panoramic video datasets with captions and in turn suggest the new PanoVid dataset consisting of 13K videos, tailored for text-to-video generation. Next, they lift a pre-trained Wan 2.1 to panoramic space by proposing three techniques: (1) latitude-aware sampling to align the initial noise with the spherical frequency of panoramic videos, (2) rotated semantic denoising and (3) padded pixel-wise decoding to achieve seamless transitions where the left and right side of the panoramic images meet. Quantitative and qualitative results demonstrate that each component significantly improves the quality for generated videos and outperforms baseline methods.", "questions": "Some critical questions regarding PanoWan's novelty are mentioned above.\n\n* Is the proposed method only applicable to Wan models, or is it generally applicable to other video models such as VideoCrafter or CogVideoX? \n\n* Moreover, are the previous methods such as DynamicScaler also applicable to Wan? Or are limited by architecture?", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strength:**\n\n* The metrics in Table 1 show that PanoWan is clearly better than the baselines 360DVD and DynamicScaler. The videos in the supplementary materials further show the PanoWan can generate high-quality panoramic videos.\n\n* PanoWan can be utilized for various zero-shot applications such as super-resolution or inpainting for panoramic videos.\n\n**Weakness:**\n\n* **Lack of novelty:** PanoWan seems to be a combination of individual engineering tricks that were already used in previous work, rather than a novel technical contribution. I listed the major concerns below:\n\n   * Rotated semantic denoising resembles the **two-end alignment** proposed in PanoDiffusion [1], which resolves the inconsistency between the left and right side a panorama by rotating the scene for every denoising step. While the task of PanoDiffusion (i.e. panorama outpainting) is not identical to PanoWan, I believe it should have been discussed as it proposes a similar approach for a similar problem in panorama generation. Please correct me if there is a major difference between the two methods.\n\n  * Moreover, the core idea of padded decoding seems quite related to StitchDiffusion [2], where they copy the leftmost part of the panorama and paste it to the rightmost part in order to ensure the contuity. I believe such previous works with heavily related approaches should have been discussed in either the related work or methods section.\n\n* **Unfair comparison:** While the quantitative numbers of PanoWan is good, it may be an overclaim that the advantage over the baselines is coming from the proposed method. 360DVD is based on Stable Diffusion and DynamicScaler is based on VideoCrafter [3], which are both clearly outdated than Wan 2.1. Given this, I am curious whether the authors can claim that each component of PanoWan is technically better than the methods in those previous works, or the advantage is rather coming from choosing a better base video model.\n\n* **Missing citation (or baseline):** DiffPano [4] is also an important related work for panoramic generation, and also a possible baseline since it generates **multi-view** panoramic images (which can be interpreted as panoramic videos).\n\nOverall, although PanoWan does seem to produce high-quality panoramic videos, its main source of strength and the novelty of each component are not presented clearly in the paper.\n\n[1] PanoDiffusion: 360-degree Panorama Outpainting via Diffusion, ICLR 2024\n\n[2] Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models, WACV 2024\n\n[3] VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models, 2024\n\n[4] DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion, NeurIPS 2024", "was_revised": true, "final_justification": "The authors' rebuttal on the raised concerns has resolved most of the critical concerns regarding the distinction of related work (StitchDiffusion and PanoDiffusion), and additional discussions/experiments with a different baseline (360DVD). Given this, I have raised my rating to **borderline accept**.", "rebuttal": "We thank the reviewers for their insightful and constructive feedback.\n\nIn accordance with the rebuttal policy, we are unable to include new qualitative results (e.g., generated videos) or external links. We assure the reviewers that, should the paper be accepted, the camera-ready version will be thoroughly updated to include all additional experiments and qualitative results discussed in this rebuttal.\n\nWe now address the specific points raised by the reviewer.\n\n**W1: Lack of novelty**\n\nWe thank the reviewer for their insightful feedback. We would like clarify our novel contributions, addressing the concerns about Rotated Semantic Denoising (RSD) and Pixel-wise Padded Decoding (PPD), respectively.\n\n- PanoFusion proposes Two-End Alignment (TEA) to apply a fixed 90° rotation to the latent code with the stated goal to \"explicitly enforce the two ends of an image to be wraparound-consistent\"  for static image outpainting. In contrast, our RSD implements a step-dependent circular shift, ensuring the cumulative prediction error is evenly distributed across all longitudes during text-based panoramic video generation. To further address the reviewer's concern, we conduct an additional experiment using a fixed 90° angle within our framework. This results in four slightly visible seams.\n\n- StitchDiffusion augments the denoising process by performing additional pre-denoising operations on a \"stitch block\" created from the image's leftmost and rightmost regions at each timestep. This method operates entirely within the latent space to influence the U-Net's predictions. In contrast, our proposed PPD targets the VAE decoding phase to prevent boundary artifacts that are re-introduced by standard VAEs (trained with zero-padding) when decoding the latent code into pixel space. Specifically, PPD pads the latent code with its own wrapped content, ensuring the decoder's convolutional kernels never process invalid boundary padding. Notably, the overhead of PPD is negligible, whereas StitchDiffusion's approach significantly increases computational cost.\n\nBeyond these points, we also propose Latitude-aware Sampling (LAS) as a zero-parameter solution to address latitude distortion at the poles. LAS introduces distortion-aware noise initialization directly into the diffusion process, ensuring spherical awareness while fully preserving the powerful generative priors of the pre-trained backbone (Sec. 4.2).\n\nWe thank the reviewer for pointing out these relevant works and will follow the suggestion to discuss them in our final version.\n\n**W2: Unfair comparison**\n\nWe would like to clarify that our primary objective with PanoWan is to demonstrate that generative priors of pre-trained conventional text-to-video generation models can be effectively lifted to the panorama with minimal equipped modules (Line 30-32). As the results in Fig. 1 and Sec. 5.5 show, PanoWan effectively preserves the generalization ability of the backbone, enabling high-quality panoramic video generation, effectively zero-shot achieving downstream applications (e.g., long-video generation, super-resolution, semantic editing, and video outpainting).\n\nHowever, our framework is indeed not limited to Wan 2.1. The core modules (LAS, RSD, and PPD) are independent of the base model and they are designed to address fundamental challenges in panoramic generation, rather than being specific to any single architecture:\n\n- LAS adapts noise initialization to spherical geometry.\n\n- RSD and PPD restructure the denoising and decoding process to improve spatial consistency and perceptual fidelity.\n\nTo empirically validate this generalizability, we conduct an additional experiment as suggested. We integrate our modules into the T2V backbone used by 360DVD and retrain them under the same settings. The results in the table below demonstrate that our modules provide a clear improvement in edge continuity when applied to the 360DVD backbone. Notably, we focus here on objective metrics, while \"motion pattern\" and \"scene richness\" are subjective and require a separate user study.\n\n| Method | FVD ↓ | VideoCLIP-XL ↑ | Image Quality ↑ | End Continuity ↓ |\n| :--- | :---: | :---: | :---: | :---: |\n| 360DVD (vanilla) | 1750.36 | 20.27 | 0.7054 | 0.0323 |\n| 360DVD + LAS/RSD/PPD | **1738.77** | **20.49** | **0.7030** | **0.0280** |\n\n**W3: Missing citation (DiffPano)**\n\nWe thank the reviewer for pointing out this relevant work. \n\nHowever, we believe there is a fundamental distinction between the task of DiffPano and our PanoWan. DiffPano is designed for multi-view panoramic image generation, whereas our PanoWan focuses on temporally coherent panoramic video generation. DiffPano generates each view independently and lacks an explicit temporal modeling mechanism, which is essential for creating smooth video sequences. This limitation is acknowledged in the DiffPano paper itself, which states: \"...as the number of frames increases during inference, the model tends to hallucinate content.\" Therefore, a direct comparison would not be meaningful. \n\nFurthermore, adapting DiffPano into a video generation model would require significant architectural modifications (e.g., adding temporal modules) and retraining. This is beyond the scope of a fair baseline comparison for this work. \n\nWe appreciate the suggestion and will ensure our final version includes a thorough discussion with it.\n\n**Q1: Base model**\n\nYes, our proposed method is generally applicable. To demonstrate this, we have integrated our modules into the 360DVD backbone and retrained it under the same settings. The detailed results of this experiment are presented in our response to W2.\n\n**Q2: Improving DynamicScaler**\n\nWe thank the reviewer for this insightful question. While DynamicScaler is theoretically applicable to Wan2.1 backbone, we argue that such an adaptation may not effectively address two fundamental limitations of its approach:\n\n1. Quality limitations. The core strategy of DynamicScaler is to generate the panorama in independent patches. This approach inherently lacks a global context, leading to repetitive patterns across different patches and a limited motion range (as movements rarely cross patch boundaries). \n\n2. Computational cost. The patch-based generation is less efficient. The official DynamicScaler takes over 360 seconds to generate a 16-frame video (448×896) on an H100 GPU. Given that Wan 2.1 backbone is more computationally intensive, a naive adaptation to Wan2.1 would be very slow. In contrast, our PanoWan generates an 81-frame video in just 90 seconds.\n\nWe will add this discussion to our final paper."}]

Author rebuttal:

Meta-review: The paper introduces technical tricks to adapt standard text-to-video diffusion models for panoramic video generation. The proposed modifications are efficient and lead to better results, but there were some concerns regarding their novelty and the experimental evaluation. During the extensive discussion the authors extended the experiments as requested. Regarding novelty, it became clear that some of the proposed measures indeed resemble ideas in prior work (which is almost unavoidable given the volume of literature produced on the topic), and the authors acknowledged this. Still, the overall package constitutes an incremental step forward. After the discussion the reviewers were satisfied and converged to a unanimously positive recommendation. The AC sees no reason to overturn that consensus.
Decision: Accept (poster)