Title: PanoWan: Lifting Diffusion Video Generation Models to 360$^\circ$ with Latitude/Longitude-aware Mechanisms
Venue: NeurIPS 2025
Accepted: yes
Keywords: Panoramic Video Generation

Video Diffusion Models
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.

PanoWan: Lifting Diffusion Video Generation Models to 360 ◦ with Latitude/Longitude-aware Mechanisms

Abstract

Panoramic video generation enables immersive 360° 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 highquality 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. Our project page is available at https://panowan.variantconst.com .

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 is highly valuable for conventional VR content, the construction of interactive game worlds [35, 8], and the simulation of environments for embodied AI [18].

The remarkable capabilities of conventional text-to-video models [28, 33, 4] motivate researchers to leverage their generative priors to panoramic video generation. One intuitive strategy generates local perspective conventional videos and integrates them during inference. While these trainingfree methods [12, 21] entirely preserve generative priors, they sacrifice overall consistency as they struggle to establish cross-view long-range dependencies. Alternatively, fine-tuning conventional text-to-video models [32, 41] also faces challenges. On one hand, existing panoramic video datasets are limited in scope and scale compared to conventional ones. On the other hand, the gap in spatial

Text-to-video generation

Long video

Semantic editing

Original

Inpainted

Change the color of the train to red.

Super-resolution

Low

resolution

High

resolution

Video outpainting

Original

Outpainted

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 training-based approach to overcome current bottlenecks. We propose PanoWan , a framework for Pano ramic video generation based on Wan 2.1 [33]. Equipped with minimal modules, PanoWan effectively lifts generative priors from a pre-trained conventional text-tovideo 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 equirectangular projection. Since text-to-video models lack continuity awareness for left and right boundaries, we achieve seamless 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 largescale data, we further introduce PANOVID. This Pano ramic Vid eo 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. As shown in Fig. 1, our framework produces panoramic videos from text descriptions, enabling long video generation. Additionally, it enables training-free editing of user-provided panoramic videos, including superresolution, semantic inpainting, and video outpainting. Extensive experiments demonstrate that

PanoWan achieves state-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 a pre-trained model to show state-of-the-art performance on panoramic video generation and robustness for downstream tasks.

We integrate the latitude-aware sampling, rotated semantic denoising, and padded pixel-wise decoding to bridge the spatial difference between panoramic and conventional videos.

2 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 [10], temporal layers [25], and attention mechanisms [3]. To further improve efficiency, LVDM [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. , HunyuanVideo [14], CogVideoX [38], and Seaweed [24]) emerge and demonstrate the benefits of scaling both model and data size. This motivates us 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

Text conditioned generation. 360DVD [32] is the pioneer to introduce stable video generation techniques to panoramic video generation by proposing a 360-Adapter and a set of 360 enhancement techniques. Training-free methods ( e.g. , DynamicScaler [12] and SphereDiff [21]) create panoramic videos by generating local patches and then composing them together into a 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 a DiT backbone with global-temporal attention and panoramic-specific losses for coherent long-range generation. Despite these advancements, existing methods still suffer from observable latitude distortions 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.

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 Gaussian splatting and a two-stage diffusion process for high-fidelity 4D reconstruction. VidPanos [20] treats panorama generation as a space-time outpainting task from panning video inputs, while Argus [19] integrates motion and geometry cues for enhanced video-to-360° synthesis. These explorations highlight a trend toward unifying spatial, temporal, and geometric reasoning. We demonstrate that PanoWan possesses these capabilities, with robust zero-shot capabilities for downstream tasks.

3 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 [32, 36, 41] mainly rely on WEB360 dataset [32], which contains only 2114 video clips of 10 seconds each. Although Argus [19] filters out over 283K video clips from the 360-1M dataset [29], it is not built for the text-based panoramic video generation task, providing no paired captions, and showing significant distribution bias for the scenario semantics.

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 3601M [29], 360+x [5], Imagine360 [26], WEB360 [32], Panonut360 [37], the Miraikan 360-degree Video Dataset [1], 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-based filtering pipeline. To transform these heterogeneous sources into a highquality paired dataset, we design a scalable five-stage filtering pipeline guided by a vision-language model. (i) Initial filtering 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) Shot segmentation: Each raw video is segmented into 10-second continuous clips using PySceneDetect, ensuring scene consistency and temporal coherence. (iii) Vision-language annotation: We employ Qwen-2.5-VL [2] to process each clip, generating a descriptive caption and predicting the associated POI (Point-of-Interest) category in a structured JSON format. Notably, we retain only clips that the model identifies as true ERP (equirectangular projection) panoramic videos. (iv) Motion score filtering: Following [7], we compute a normalized optical-flow magnitude and remove clips with a motion score below 0.4 to exclude static or nearly still scenes. (v) Aesthetic score filtering: Finally, we utilize Q-Align [34] to evaluate the aesthetic quality of each frame, discarding clips with a 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 underrepresented 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. It thereby provides a robust foundation for training and evaluating text-conditioned panoramic video generation models.

4 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 a small subset of parameters via LoRA [11]. 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 (Sec. 4.2). Finally, we present the rotated semantic denoising and the padded pixel-wise decoding to achieve the seamless longitude transitions (Sec. 4.3).

4.1 Preliminaries

Video diffusion models. We employ Wan 2.1 [33] as the video generation backbone, with spatialtemporal Variational AutoEncoders (VAEs) to map high-dimensional videos into compact latent codes. The flow matching framework [17] is used to model a unified denoising diffusion process. Specifically, given a clear video x , a V AE encoder E( · ) first projects the video into the latent space z 1 = E( x ) . During training, a noise z 0 ∼ N (0 , I ) is sampled, and an intermediate latent code z t = tz 1 +(1 -t ) z 0 is constructed by linearly interpolating between z 1 and z 0 at timestep t ∈ [0 , 1] .

The training goal is to predict the ground truth velocity v t = d z t / d t = z 1 -z 0 , and the loss function is formulated as:

where c txt is the text embedding, θ is the parameters of the prediction model, and u ( z t , c txt , t ; θ ) is the predicted velocity of the model.

Spherical coordinate mapping. Panorama captures a 360° view, inherently representing signals in spherical coordinates ( φ, θ ) . To leverage generative priors from conventional images and videos that operate in Cartesian coordinates ( x, y ) , we employ the equirectangular projection (ERP) P ERP to map between these coordinate systems for panoramic videos:

where R is the radius of the sphere. φ and θ are longitude and latitude respectively. While ERP enables the direct application of pre-trained V AEs 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 is recognized by changes in horizontal signal frequency. Let d s φ and d s θ represent the infinitesimal arc lengths along lines of constant latitude and longitude, respectively. They are formulated as:

where θ is the latitude. We further consider the spherical frequency f sph (cycles per unit physical distance) and the Cartesian frequency f car (cycles per pixel in the image). Assuming that warping preserves content, their relationship in frequency is scaled by the change in distance:

Consequently, in polar regions ( | θ | ≈ π 2 , namely y ≈ 0 or y ≈ R ), the horizontal frequency in the Cartesian coordinate becomes near-zero ( f car ,x ( y ) ≈ 0 ). Such distortion in the horizontal frequency distribution significantly degrades the effectiveness of transferring priors.

4.2 Latitude-Aware 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 ( x, y ) . To avoid latitudinal distortion in polar regions of ERP, we propose the latitude-aware sampling to 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, we calculate the sampling noise by remapping the horizontal sampling coordinate x based on the latitude corresponding to row y , and then applying the interpolation:

where P ′ ( x, y ) is the interpolated noise vector at coordinate ( x, y ) . Interp ( · ) is formulated as the interpolation function for normalization:

Latent diffusion

Padded decoding

Initial noise

Latitude aware

sampling

Text prompt

Text encoder

Reconstructed video

Remove padding

Spheric noise

Input video

Rotated denoising

VAE

encoder

VAE

decoder

LoRA fine-tuning

Fixed parameters

DiT

where sgn ( · ) is the sign function, and BI ( P, x, y ) is the standard bilinear interpolation for vector P at coordinate ( x, y ) . Consequently, the resulting noise vectors sampled by our strategy preserve E [ P ′ ( x, y )] = 0 and E [ Var P ′ ( x, y )] = 1 , 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 of 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 f max, and the maximal frequency in the Cartesian coordinates at the latitude of θ is determined by:

where the equation only holds when θ = 0 , namely on the equator. For the proposed design, it guarantees max f car ,x = 2 R along the equator as cos( θ ) = 1 and the original pixels along P ( · , R -1 2 ) is taken. Therefore, the maximal spherical frequency is f max = 2 . According to Eq. (5), the warped results only depends on the values between ( R -R cos( θ ) , y ) and ( R +( R -1) cos( θ ) , y ) in the original Cartesian grid P . According to DFT, the support of the spectrum is reduced to:

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.

Rotated semantic denoising. The video generation backbone inherently introduces semantic inconsistency 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 s t ( · ) 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 Z t by { s t = t mod W } columns at denoising step t and then undo the shift:

where ϕ θ ( · ) is the noise predictor. As a result, the inherent accumulative error for horizontal coordinate x after T denoising steps is:

where ε t ( · ) is prediction error for the transition and step t , which would concentrate at a fixed seam if no rotation are applied. Due to the rotation strategy, this error at physical coordinate x at step t is determined by the logical position { ( x + s t ) mod W } . Over T steps, these logical coordinates { ( x + s t ) mod W } T t =1 ideally approach a uniform permutation for all longitudes. This effectively suppresses seam artifacts by a 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 [19]. Inspired by previous works [30, 40], we present the padded pixel-wise decoding. Let Z 0 be the denoised latent code. We first create a padded latent code Z ′ 0 = P r ( Z 0 ) , where P r ( · ) is a circular padding operator that extends Z 0 by r columns of context on the side, and the content at horizontal coordinate x is { x mod W } in Z 0 . Finally, we center crop the decoded panoramic videos after the decoding V = Crop ( D( Z ′ 0 ) ) , as 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

PanoWan is built on Wan 2.1-1.3B-T2V [33] as the video generation backbone. We train PanoWan at a resolution of 448 × 896 , closely matching the pre-trained resolution of this backbone model. For parameter-efficient training, LoRA [11] with a rank of 64 is applied to the query, key, value, and output projections of the attention mechanisms, as well as to the feed-forward networks. The model is trained for 200K iterations on our contributed PANOVID dataset. The training process employs the AdamW optimizer [13] with a learning rate of 1 × 10 -4 and a batch size of 8. Training is conducted 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 total parameters.

5.2 Panoramic Video Evaluation Metrics

Existing panoramic video generation methods either directly apply conventional video evaluation metrics [12, 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 [12], we also

calculate specific metrics for image quality. To adapt these general metrics for panoramic videos, we project each video onto a cube map and compute metric scores separately on each of the six faces. The final reported score for a video v is a weighted average:

where F denotes the set of cube map faces, P f ( v ) is the projection of video v onto face f ∈ F , Φ is the metric function, and α f is the weight assigned to face f . Following OmniFID [6], we assign weights α top = α bottom = 1 3 and α side = 1 12 for each of the four lateral faces.

Panoramic metrics. Following previous works [32, 12], we evaluate motion patterns and scene richness with user preferences. We additionally introduce a 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 at the longitude seam.

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. 1, PanoWan achieves state-of-the-art performance across both general and panoramic metrics (detailed in Sec. 5.2). 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 effectively maintains global consistency and visual coherence, achieving superior performance in generating high-fidelity panoramic videos.

Following 360DVD [32], we also conducted a human-preference study to complement automatic metrics. 25 participants compared videos generated from 50 random test prompts among PanoWan, 360DVD [32], and DynamicScaler [12]. PanoWan was preferred in 71.36% of cases, versus 16.72% and 11.92% for 360DVD [32] and DynamicScaler [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: latitudeaware sampling (LAS), rotated semantic denoising (RSD), and padded pixel-wise decoding (PPD).

Quantitative results. As shown in Tab. 1, removing LAS primarily affects general metrics-FVD increases from 1281.21 to 1520.69, and VideoCLIP-XL drops from 21.86 to 21.20-indicating 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.

Ours

Ours

360DVD

DynamicScaler

Ours

360DVD

DynamicScaler

Wide-angle panoramic interior capturing an energetic hot pot restaurant bustling with diners enthusiastically cooking ingredients in bubbling pots. Colorful plates arranged invitingly, steam rising, spirited conversations filling warm, inviting atmosphere emphasizing community and culinary enjoyment.

Expansive panoramic view capturing a vibrant ski resort nestled among towering snowy mountains, as skiers gracefully descend pristine slopes amid cozy alpine chalets. Chairlifts glide leisurely under crisp blue skies while visitors gather around lively outdoor cafes, soaking in the sunny winter scenery.

Qualitative results. We further provide qualitative evaluation in Fig. 4. When generating highlatitude elements like LED panels (which should appear straight in perspective views but are inherently distorted in the equirectangular projection), PanoWan without LAS fails to render them with the correct geometric appearance. When RSD is discarded, semantic inconsistencies become apparent at the longitude seam, due to 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 at boundaries. Consequently, the full PanoWan model with all modules enabled achieves the best performance.

5.5 Application

As a text-based panoramic video model, PanoWan shows robust zero-shot capabilities across a wide range of downstream tasks. We present representative examples in Fig. 1 and additional examples in supplementary materials due to the space limitation.

Long video generation. To generate panoramic videos longer than the model's native temporal context, we adopt a 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 an 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 lowresolution ones, we first encode each low-resolution video into its corresponding latent code. After

(a) Visual evaluation of LAS

(b) Visual evaluation of RSD and PPD

W/o RSD

Equirectangular

Perspective

W/o LAS

W/ LAS

Perspective

Equirectangular

W/o PPD

Full model

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 a panoramic video, we identify and mask regions for modification. Next, we apply the denoising process to these regions, guided by user-provided text descriptions. Leveraging its understanding of spherical representations, the inpainted content naturally exhibits the properties of ERP projection.

Outpainting for conventional videos. Similar to the inpainting process, we first map the conventional video to the latent code and then mask the surrounding unseen panoramic regions. With user-provided text descriptions, we denoise the masked regions to 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, we contribute the PANOVID dataset, offering high-quality and semantically rich 360° video data with annotated text descriptions. Extensive experiments demonstrate that PanoWan achieves state-of-the-art 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, it is also inherits a common challenge: the content forgetting problem often seen in such models. This issue is particularly evident when generating long videos due to limited temporal memory. We believe this challenge can be 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 No.62136001. Authors thank openbayes.com for providing computing resources.

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Jing Tan, Shuai Yang, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv preprint arXiv:2412.03552 , 2024.

Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717 , 2018.

Veo-Team, :, Agrim Gupta, Ali Razavi, Andeep Toor, Ankush Gupta, Dumitru Erhan, Eleni Shaw, Eric Lau, Frank Belletti, Gabe Barth-Maron, Gregory Shaw, Hakan Erdogan, Hakim Sidahmed, Henna Nandwani, Hernan Moraldo, Hyunjik Kim, Irina Blok, Jeff Donahue, José Lezama, Kory Mathewson, Kurtis David, Matthieu Kim Lorrain, Marc van Zee, Medhini Narasimhan, Miaosen Wang, Mohammad Babaeizadeh, Nelly Papalampidi, Nick Pezzotti, Nilpa Jha, Parker Barnes, Pieter-Jan Kindermans, Rachel Hornung, Ruben Villegas, Ryan Poplin, Salah Zaiem, Sander Dieleman, Sayna Ebrahimi, Scott Wisdom, Serena Zhang, Shlomi Fruchter, Signe Nørly, Weizhe Hua, Xinchen Yan, Yuqing Du, and Yutian Chen. Veo 2. 2024. URL https://deepmind.google/technologies/veo/veo-2/ .

Matthew Wallingford, Anand Bhattad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an image to a scene: Learning to imagine the world from a million 360° videos. In Proc. of Neural Information Processing Systems , 2024.

Hai Wang, Xiaoyu Xiang, Yuchen Fan, and Jing-Hao Xue. Customizing 360-degree panoramas through text-to-image diffusion models. In Proc. of IEEE/CVF Winter Conference on Applications of Computer Vision , 2024.

Jiapeng Wang, Chengyu Wang, Kunzhe Huang, Jun Huang, and Lianwen Jin. VideoCLIPXL: Advancing long description understanding for video clip models. arXiv preprint arXiv:2410.00741 , 2024.

Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360DVD: Controllable panorama video generation with 360-degree video diffusion model. In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024.

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Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Chunyi Li, Liang Liao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtai Zhai, and Weisi Lin. Q-Align: Teaching LMMs for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090 , 2023.

Zeqi Xiao, Yushi Lan, Yifan Zhou, Wenqi Ouyang, Shuai Yang, Yanhong Zeng, and Xingang Pan. WORLDMEM: Long-term consistent world simulation with memory. arXiv preprint arXiv:2504.12369 , 2025.

Kevin Xie, Amirmojtaba Sabour, Jiahui Huang, Despoina Paschalidou, Greg Klar, Umar Iqbal, Sanja Fidler, and Xiaohui Zeng. VideoPanda: Video panoramic diffusion with multi-view attention. arXiv preprint arXiv:2504.11389 , 2025.

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Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. CogVideox: Text-to-video diffusion models with an expert transformer. In ICLR , 2025.

Lijun Yu, José Lezama, Nitesh B. Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Vighnesh Birodkar, Agrim Gupta, Xiuye Gu, Alexander G. Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A. Ross, and Lu Jiang. Language model beats diffusion - tokenizer is key to visual generation. In ICLR , 2024.

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Muyang Zhang, Yuzhi Chen, Rongtao Xu, Changwei Wang, JinMing Yang, Weiliang Meng, Jianwei Guo, Huihuang Zhao, and Xiaopeng Zhang. PanoDit: Panoramic videos generation with diffusion transformer. In Proc. of the AAAI Conference on Artificial Intelligence , 2025.

Haiyang Zhou, Wangbo Yu, Jiawen Guan, Xinhua Cheng, Yonghong Tian, and Li Yuan. Holotime: Taming video diffusion models for panoramic 4D scene generation, 2025. URL https://arxiv.org/abs/2504.21650 .

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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\n\nQUESTIONS:\nSome 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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).\n\nQUESTIONS:\nAbout 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:

+ The generated results look good with high-quality details and consistent polar patterns.
+ Latitude-aware sampling is new, convincing, and validated effective by ablations.
+ The framework shows zero-shot capabilities by investigating different types of downstream tasks, including important tasks such as video outpainting, video super-resolution, etc.

Weaknesses:
- 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.
- 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.
- 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?)

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
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}