Title: Imagine360: Immersive 360 Video Generation from Perspective Anchor
Venue: NeurIPS 2025
Accepted: yes
Keywords: video generation

video outpainting

panorama video generation

360 video generation
Abstract: $360^\circ$ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. 
To achieve more accessible and personalized content creation in $360^\circ$ video format, we seek to lift standard perspective videos into $360^\circ$ equirectangular videos. To this end, we introduce **Imagine360**, the first perspective-to-$360^\circ$ video generation framework that creates high-quality $360^\circ$ videos with rich and diverse motion patterns from video anchors.
Imagine360 learns fine-grained spherical visual and motion patterns from limited $360^\circ$ video data with several key designs. 
**1)** Firstly we adopt the dual-branch design, including a perspective and a panorama video denoising branch to provide local and global constraints for $360^\circ$ video generation, with motion module and spatial LoRA layers fine-tuned on $360^\circ$ videos.
**2)** Additionally, an antipodal mask is devised to capture long-range motion dependencies, enhancing the reversed camera motion between antipodal pixels across hemispheres.
**3)** To handle diverse perspective video inputs, we propose rotation-aware designs that adapt to varying video masking due to changing camera poses across frames.
**4)** Lastly, we introduce a new 360 video dataset featuring 10K high-quality, trimmed 360 video clips with structured motion to facilitate training.
Extensive experiments show Imagine360 achieves superior graphics quality and motion coherence with our curated dataset among state-of-the-art $360^\circ$ video generation methods. We believe Imagine360 holds promise for advancing personalized, immersive $360^\circ$ video creation.

Imagine360: Immersive 360 Video Generation from Perspective Anchor

Abstract

360 ◦ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees. To achieve more accessible and personalized content creation in 360 ◦ video format, we seek to lift standard perspective videos into 360 ◦ equirectangular videos. To this end, we introduce Imagine360 , the first perspective-to360 ◦ video generation framework that creates high-quality 360 ◦ videos with rich and diverse motion patterns from video anchors. Imagine360 learns fine-grained spherical visual and motion patterns from limited 360 ◦ video data with several key designs. 1) Firstly we adopt the dual-branch design, including a perspective and a panorama video denoising branch to provide local and global constraints for 360 ◦ video generation, with motion module and spatial LoRA layers fine-tuned on 360 ◦ videos. 2) Additionally, an antipodal mask is devised to capture long-range motion dependencies, enhancing the reversed camera motion between antipodal pixels across hemispheres. 3) To handle diverse perspective video inputs, we propose rotation-aware designs that adapt to varying video masking due to

changing camera poses across frames. 4) Lastly, we introduce a new 360 video dataset featuring 10K high-quality, trimmed 360 video clips with structured motion to facilitate training. Extensive experiments show Imagine360 achieves superior graphics quality and motion coherence with our curated dataset among state-ofthe-art 360 ◦ video generation methods with both real and generated videos. We believe Imagine360 holds promise for advancing personalized, immersive 360 ◦ video creation.

1 Introduction

Imagine embarking on a journey through the heart of a bustling city, a serene beach, or a cherished place of your own. It would be wonderful to record this 360-degree dynamic experience for future viewing. 360 ◦ video offers an interactive, immersive format that captures a living, breathing world as if the viewer were part of the experience. With the rapid development of VR devices, the demand for 360 ◦ videos is increasing, leading to growing focus on 360 ◦ video generation research.

Recent advancements in 360 ◦ video generation focused on text-guided [34] and image-guided [18] models. While these methods produce plausible 360 ◦ videos, they require panoramic optical flow [34] or high-quality panoramic images [18] as guidance, which are hard to obtain for users. In contrast, traditional perspective videos are more accessible, as they can be easily captured using smartphone cameras or generated by advanced video synthesis models. To enable more user-friendly and personalized 360 ◦ video creation, we propose a new task: perspective-to360 ◦ video generation, which transforms standard video inputs into 360 ◦ equirectangular videos. Specifically, we take a perspective video with narrow FOV as the anchor video, project it to a 360 ◦ × 180 ◦ FOV video canvas, and synthesize the surrounding pixels.

One relevant task is video outpainting [2; 30; 8], which aims to fill in missing regions outside the edges of video frames in a larger canvas, typically in the perspective domain with fixed video masking across frames. Simply applying standard video outpainting methods does not achieve satisfactory results, as our perspective-to360 ◦ video generation exhibits more challenges. First, due to the large domain gap between perspective and 360 videos, learning the spherical visual and motion patterns requires sophisticated design when trained on limited 360 video data. Second, as videos exhibit different camera poses across frames, after mapping to the 360 canvas, the video mask would change significantly in shape, size, and location, requiring rotation-aware designs for robust generation.

To address these challenges, we introduce Imagine360, the first framework to generate high-quality 360° videos from standard perspective video inputs. Inspired by the dual-domain concept [42] in textto-image generation, our model employs dual-branch video outpainting Unets in the perspective and panoramic domains. These two branches jointly denoise the partially masked 360 spacetime canvas in the global scope and in each perspective window. A cross-domain attention module establishes interactions between dual-branch latents that map to the same position in the 360 sphere, enabling high-quality and plausible spherical video patterns. While positionally aligned latents offer local consistency, they fall short in accounting for a unique characteristic of panoramic videos, where each pixel undergoes reverse camera translation of its antipodal counterpart. Hence, we improve the cross-domain attention with an antipodal mask , to extend each pixel's receptive field from its local neighborhood to its antipodal region across the 360 sphere, making it easier to learn long-range panoramic motion dependencies. Another key challenge lies in the varying input camera poses. Handling general videos as anchors requires the framework to accommodate changing camera poses across frames. In practice, we incorporate rotation-aware designs , including rotation-aware data sampling in training and a camera pose estimation module in inference. Through explicit tracking of the rotating input video in the 360 spacetime canvas, our model streamlines robust generation from customized video inputs. Effective training of our framework also relies on high-quality 360° video data. However, existing datasets are either small in scale [34; 37; 38] or consist of large-scale, unfiltered web-scraped content that requires extensive cleaning [28]. In this regard, we introduce YouTube360, a ready-to-train 360° video dataset comprising 10K curated clips from YouTube. Our dataset incorporates manual quality control and sophisticated data cleaning to select high-quality training segments with diverse and structured motion.

With these three key designs and our curated dataset, Imagine360 pioneers end-to-end, high-quality 360 ◦ video generation from perspective inputs. Extensive experiments show that our model achieves

state-of-the-art performance in both frame quality and motion consistency. As a bonus, our pipeline also demonstrates superior results in panoramic image outpainting. We believe Imagine360 can empower the 360 ◦ video generation community to create personalized, hyper-immersive experiences for real-world applications.

2 Related work

2.1 Video outpainting

Video Outpainting aims to fill in the missing regions at the edges of source videos. Compared to advanced image-level outpainting, video-level outpainting remains under-explored due to its challenges in maintaining both spatial and temporal fidelity and consistency. Recent video outpainting methods leverage diffusion to generate high-quality pixels in the missing regions. M3DDM [8] proposes a frame-guided Masked 3D diffusion model and a coarse-to-fine inference strategy to tackle artifact accumulation in long video outpainting. MOTIA [30] employs a per-case optimization strategy to learn the data-specific patterns of source video for better outpainting quality. Follow-YourCanvas [2] divides the canvas into multiple windows and achieves outpainting of different sizes and resolutions by merging each outpainted window. These methods focus on handling perspective video outpainting with fixed masking in each frame. Despite their appealing outpainting results, it remains difficult for them to handle perspective360 ◦ video generation that requires high-quality outpainting in panoramic distribution and continuously changing video masks from varying camera poses. In contrast, our Imagine360 handles the perspective-to360 ◦ video generation with global and local constraints from dual-branch diffusion and rotation-aware designs to handle changing video masks, producing high-quality 360 ◦ video generations from perspective video anchors.

2.2 360 panorama generation

Early methods [31; 4; 1; 5; 15; 21; 35] exploit GAN-based framework for panorama image generation [19; 27]. OmniDreamer [1] proposes transformer-based framework for 360-degree outpainting and devises circular inference to obtain 360 ◦ close-loop continuity. Recently, diffusion-based methods [33; 17; 36; 42; 32; 9; 16; 26; 43] have dominated image-level panorama generation. Due to the data scarcity of large panorama image datasets, methods [33; 32] that directly fine-tune LDM to generate the panorama results in low-quality images with simple structures and sparse assets. PanFusion [43] introduces panorama and perspective branches to leverage the synergy from both global and local constraints for text-to-panorama generation.

Despite numerous efforts in image-level panorama generation, there are few works [34; 18; 20] focusing on panorama video generation. 360DVD [34] takes text prompts and additional panorama video optical flow as guidance and learns a 360-Adapter on standard T2V models to generate plausible 360 ◦ videos. 4K4DGen [18] animates a static panoramic image at user-selected regions with I2V pre-trained prior in a training-free manner. These fine-tuning-based or training-free approaches struggle to bridge the distribution gap between panoramic and perspective videos, resulting in simple natural perturbations, such as clouds moving and water running. In contrast, our Imagine360 benefits from the dual-branch video denoising structure with antipodal relation modeling and rotation-aware designs, resulting in more dynamic 360 ◦ videos with rich and structured motions.

3 Our approach

Given a perspective video, we aim to generate an equirectangular (ERP) 360 video that replicates its visual appearance and motion, and extends to the complete 360 ◦ × 180 ◦ field of view.

We propose Imagine360, the first perspective-to360 ◦ video generation framework, incorporating three key designs to synthesize high-quality panoramic videos, as illustrated in Fig. 2. First, to learn the spherical visual and motion patterns based on pre-trained perspective generative prior, we employ the dual-branch design, consisting of a panorama branch and a perspective branch, to jointly denoise the 360 ◦ spacetime latent. Second, to obtain more fine-grained and plausible panoramic motion, we refine the cross-domain attention to highlight antipodal masking that captures long-range motion dependencies in antipodal directions. Lastly, to handle diverse video inputs with varying camera

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Target Panorama Video

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~𝒩(0,1)

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Pano

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1

alighthouse

oharocky

overlooking

the ocean.

poses, we propose rotation-aware training and inference designs to obtain robust generations from different video anchors.

3.1 Preliminary: 360 video format

360 ◦ video is a video format that captures a full horizontal 360° field of view (FOV) and vertical 180° FOV. Unlike standard perspective videos, which are limited to a fixed, narrow field of view, 360° video enables viewers to look in any direction at each frame location, creating an immersive experience. For encoding, storage, and playback, 360 video must be projected onto a 2D plane from the 3D sphere. In our work, we use 360 videos encoded by Equirectangular Projection (ERP).

To create perspective video frame from ERP video frame of resolution H × W , we simulate a virtual pinhole camera looking in a specific direction defined by yaw ( θ ), pitch ( ϕ ) and roll ( ψ ) Euler angles and FOV parameter. Each pixel on the image plane is computed with a direction vector ⃗ v ij = [1 , 2 j W -1 , -( 2 i H -1 ) ] T · tan ( FOV 2 ) in 3D space and is rotated according to the desired viewing orientation ⃗ v ′ = R ψ · R ϕ · R θ · ⃗ v ij given the Euler angles. Then, these vectors are converted into spherical coordinates λ = arctan 2( ⃗ v ′ y , ⃗ v ′ x ) , ϕ = arcsin( ⃗ v ′ z ) and mapped to 2D

ERP coordinates u = ( λ + π 2 π ) · W,v = ( 1 -ϕ + π 2 π ) · H for sampling. We denote this process as the Equirectangular-to-perspective (E2P) mapping. The inverse mapping, denoted as P2E mapping, maps a perspective video frame back to ERP frame. The mapping begins by computing a 3D directional vector in the spherical coordinates for each pixel in the ERP frame λ = 2 π · ( u W ) -π, ϕ = π 2 -π · ( v H ) , v = [cos( ϕ ) · cos( λ ) , cos( ϕ ) · sin( λ ) , sin( ϕ )] T , then rotate the vector inversely into the perspective camera's reference frame and projected onto the image plane. Pixels that fall within the camera's FOV sample the perspective pixel value and write back to the ERP frame.

3.2 Video-conditioned 360° video generation

Our model is trained on triplets of 360° videos, text captions, and corresponding 360° video masks. Using E2P (Equirectangular-to-Perspective) mapping, the 360° video mask determines the anchor perspective video extracted from the full 360° video. To generate different training masks, we sample sets of Euler angles ( θ 1: T , ϕ 1: T , ψ 1: T ) over T frames via P2E (Perspective-to-Equirectangular) mapping, where θ denotes yaw, ϕ pitch, and ψ roll. Similar to previous inpainting models [23; 2], our model takes as input a concatenation of the noisy 360° video latent, the video mask, and the masked 360° video latent along the channel dimension. A frozen VAE encodes both the full and masked 360° video inputs into d -dimensional latent channels. Additionally, we project the masked region of the 360° video into the perspective anchor video to provide fine-grained visual guidance. Following [2], the anchor video I 1: T anc is encoded into semantic features using the visual encoder from SAM [14]. These features are then processed by a pre-trained query-based Transformer, which distills high-level

visual and motion cues into compact latent tokens. The visual tokens are concatenated with the text embeddings in the conditioning branch. To guide large-scale spacetime pixel generation, we incorporate IP attention [41], which decouples cross-attention for injecting text prompts and visual tokens in the U-Net layers. This enables our model to effectively propagate visual content and motion patterns across views and into unmasked regions of the spacetime panorama.

3.3 Dual-branch 360° video denoising

Dual-branch design. Extending a perspective video to a 360° canvas requires careful model design due to the large differences between panoramic and perspective distributions. Mainstream methods either train a Latent Diffusion Model (LDM) to directly denoise panorama frames or jointly denoise multiple perspective view projections. The former results in plain visuals and mild motion from limited 360 data, while the latter easily produces local, short-range motions in each view. Inspired by image-level generation [42], we employ a dual-branch video denoising structure. It consists of a global panorama branch and a local perspective branch based on U-Net structure, where each block consists of spatial layers initialized from SD weights and a motion module initialized from [2] weights. The two branches share the same input: the panoramic branch latents of shape R T × (2 d +1) × h × w is projected into m = 20 perspective views according to the P2E icosahedron modeling [22; 42], and each view gets latent of shape R T × (2 d +1) × h/ 2 × h/ 2 through projection. In the panoramic branch selfattention, the model gains a holistic modeling of the 360 ◦ spacetime canvas for global consistency. In the perspective branch, the latent permutes and flattens the number of views with the batch size for selfattention to preserve the pre-trained generative power in the local window. In each U-Net downsample block, the latents from both branches are aligned via cross-attention to enhance the local-global synergy. The perspective latents z 1: T P ∈ R T × m × D × h/ 2 × h/ 2 are reshaped to R T × D × ( m × h/ 2 × h/ 2) , then bidirectionally cross-attended with panorama latents z 1: T E ∈ R T × D × ( h × w ) , where D is the channel dimension, h, w are the latent spatial dimension. Spherical positional encoding [42] is added to indicate the relative spherical location. A spherical attention mask on the attention map enforces E2P and P2E mapping between the flattened latent sequence from the two branches. As illustrated in Fig. 3, the directly mapped pixels via P2E mapping in the perspective branch are highlighted in orange. Gaussian blur is also applied to the spherical mask to enable neighboring activations in the attention.

Resource-friendly fine-tune strategy. With only thousands of training panorama videos, we seek to fine-tune a limited proportion of parameters to yield good generation results. Thanks to the space-time disentangled design from AnimateDiff [10], we do not need to fine-tune the whole model to learn the distributions of panoramic videos. In practice, we add LoRA on spatial attention layers and IP attention layers for both branches. As for the panoramic motion, we show in supp. that employing motion LoRA layers does not have sufficient influence on the pretrained prior to generate good panorama motion. Therefore, the whole motion module is fine-tuned for the panorama branch to learn the necessary prior for generating spherical motion patterns. Note that, to fully exploit the perspective generative prior, we do not fine-tune the motion module in the perspective branch. Experimental results show that our resource-friendly fine-tuning strategy achieves competitive results for 360 ◦ video generation despite the limited data and GPU resources. The training objectives in the two branches are:

where x 1: T · and ϵ · , Θ denote the target equirectangular ( E ) and perspective ( P ) video and its predicted noise, y is the text prompt. To facilitate the synchronous exchange of information between two branches, we combine the two branches losses as the final training objective L = L E + 1 m ∑ m i =1 L i P .

3.4 Encouraging fine-grained panoramic patterns

The dual-branch design generates plausible spherical patterns for spatial appearance and motion, however, to achieve fine-grained panoramic videos, i.e., 360 ◦ close-loop continuity and reversed antipodal translation, we introduce additional advanced techniques, including circular padding and antipodal mask in cross-domain spherical mask.

Circular padding for close-loop continuity. The 360 ◦ closed-loop continuity ensures seamless alignment between the left-most and right-most edges of panoramic videos. To preserve this property and mitigate artifacts from local convolutions, we follow [42; 39] by applying circular padding before each convolutional layer in the U-Net blocks, then unpadding afterwards to restore the original resolution. For improved 360° continuity, we adopt circular padding in the optional video super-resolution [11] to process upsampled latents during decoding.

Antipodal mask for reversed translational motion. One distinctive property of panoramic motion is that antipodal pixels, i.e., those located on opposite sides of the 360° sphere, often exhibit reversed motion under camera translation. Specifically, if the camera moves forward relative to the forward viewing direction, the scene observed from the backward viewing direction should appear to move away from the viewer. However, in both the panoramic and perspective branches, antipodal pixels are typically distant in the token sequence. Since attention modules tend to focus on local neighborhoods, we ob-

Q

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serve that motion in antipodal directions tends to be less evident compared to motion in nearby pixels of the anchor video. To address this, and to better emphasize antipodal relationships in 360° video generation, we introduce the antipodal mask: a new activation mask for the cross-domain spherical attention. As illustrated in Fig. 3, we identify, for each latent in the perspective domain, its corresponding antipodal latent (shown in blue) in the panorama domain, and vice versa. These antipodal pairs are assigned higher activation values in the attention mask to encourage long-range attention across opposite directions. Additionally, we apply a Gaussian blur to the mask to softly include antipodal neighbors.

3.5 Rotation-robustness over general video input

Commonly, we assume the video input to be upright with a fixed camera pose, but in-the-wild perspective videos are of various rotation angles ( θ 1: T , ψ 1: T and ϕ 1: T ). The change of rotation angle significantly impacts the shape and location of the masking, as shown in Fig. 6. To obtain robust generation for in-the-wild video inputs, we propose rotation-aware data sampling in training and camera pose estimation during inference.

Rotation-aware data sampling. We create flexible masking for each frame with a rotation-aware data sampling strategy. For simplicity, we randomly sample ϕ ∈ ( -20 ◦ , 20 ◦ ) , θ ∈ ( -45 ◦ , 45 ◦ ) , and ψ ∈ ( -10 ◦ , 10 ◦ ) for each frame, then generate smooth angle sequences that are either monotonic or back-and-forth within the sampling range. Moreover, we add the sinusoidal positional embedding of the mask and the Euler angles to the conditioning tokens, i.e., the visual tokens of the anchor video and the text embedding, in order to indicate the relative position of the mask and the global canvas. For mask positional embedding, we compute based on its maximum inscribed box coordinates. The positional embeddings are concatenated along the channel dimension.

Camera pose estimation for inference. During inference, we employ MonST3R [44] to estimate the camera pose for each frame in the test video. The Euler angles are calculated from the extrinsic matrix at each frame. We do not normalize the Euler angle sequence according to the first frame, as sometimes the first frame does not map to the center region of the 360 canvas. We create the 360 video mask and masked 360 video pixels based on the estimated Euler angles. The masked video pixels are encoded by VAE and concatenated with the 360 video mask and noise as inference input.

3.6 YouTube360 dataset

Fine-tuning for 360° motion generation requires substantial text-video paired data due to the domain gap between panoramic and perspective motions. Existing datasets are insufficient: WEB360 [34] is small and limited to simple landscape videos, while 360-1M [28], although large in scale, contains unfiltered low-quality videos with incomplete views and polar artifacts. Cleaning such data requires large time and computational costs.

To address this, we curate a high-quality 360 video dataset, YouTube360, from web videos. We collected 360-degree videos from YouTube, covering city tours, wildlife documentaries, and VR game captures. We prioritize long videos with diverse content, as they maintain a consistent format, offer varied scenes, and are easier to discard if polar artifacts are present. All videos are manually reviewed to remove sensitive content and low-quality samples with incomplete views or polar distortions. Then, the videos are converted to equirectangular format, resized to 512 × 1024 , and segmented with TransNet-v2 [25]. For slow-paced clips, we apply 2× speed-up. To filter out static content, we compute optical flow using PanoFlow [24] and discard clips where fewer than 10% of frames exceed an average flow magnitude of 0.1. Captions are generated using VideoLLaMa-2 [6]. The final dataset contains 9,558 five-second clips at 20 fps. Each annotated sample includes a caption, a YouTube video ID, and a time interval. We provide annotations with documentation in the supplementary. Please refer to the supplementary material for additional details.

4 Experiments

4.1 Implementation details

Training settings. The spatial and motion modules are respectively initialized on Stable Diffusion v2.1 and [2]. Training is conducted on 8 NVIDIA A100 GPUs in 50k training steps, with the spatial LoRA rank and α LoRA set to 32 and 1.0. The training resolution H × W is set to 256 × 512 , the length of frames to 40, the batch size to 1, and the learning rate to 1 × 10 -5 . For inference, generating a video of 512 × 1024 resolution and 32 frames takes approximately 6 minutes on average, using a single NVIDIA A100 GPU with 39 GB VRAM.

Evaluation metrics. We collect a test benchmark by randomly sampling videos from 360-1M [28], RealEstate10K [45], and CogVideoX [40] generations with prompts from GPT-4o [13], including both real videos and generated videos. We also randomly select 15 videos from the benchmark for the user study. Following previous practice, we employ Imaging Quality (IQ), Aesthetic Quality (AQ), Motion Smoothness (MS), and Subject Consistency (SC) metrics from VBench [12] to measure the graphics quality and motion consistency. To measure 360 motion correctness, we report Endpoint Error (EPE) on video optical flow (OF) with groundtruth 360 videos from 360-1M [28]. The EPE metric is adapted from optical flow to evaluate motion accuracy. We compute optical flow for both the generated video V gen and the ground-truth V gt using PanoFlow [24], and define EPE as the pixel-wise Euclidean distance between the two flows: EPE ( V gen , V gt ) = | V gen -V gt | . Small EPE indicates the higher similarity of generated video OF with Groundtruth OF. To measure omnidirectional video quality, we report OmniFVD that extends from OmniFID [7]. OmniFVD projects the 360 video into six cubemap views, computes FVD on each view, and reports the average as the final score.

Comparison methods. As the first perspective-to360 ◦ video generation framework, it's infeasible to find a method that has the exact same input condition as ours. We compare with methods that produce 360 videos from various guidance. 360DVD [34] is the advanced text-guided 360 ◦ video generation method that takes text prompts and panorama video optical flow as input for 360 ◦ video generation. Here, we feed the same text prompt as ours with GT optical flow into 360DVD for comparison. Follow-Your-Canvas [2] is the state-of-the-art video outpainting method that employs tile-based outpainting to handle outpainting of arbitrary size and resolution. Here, we project our perspective anchor video into the panorama canvas and feed the masked canvas into [2] for evaluation. AnimateDiff+LatentLab360 [10] takes image inputs and animates with the LatentLab360 LoRA. For comparison, we feed the first frame of our generations into AnimateDiff+LatentLab360 and take its animated videos for evaluation.

AnimateDiff+

Ours

LatentLabs360

Follow-Your-Canvas

360DVD (T2V)

Frame = 0

Frame  = T/2

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Two elephants walking on a grassy path under a blue sky.

Kayaker navigating rapids surrounded by mountains.

4.2 Quantitative comparisons

Tab. 1 provides the quantitative comparison with baseline models. Imagine360 outperforms the existing 360 video generation methods at both ground-level perspective views and overall 360 correctness. 360DVD often produces blurred videos, resulting in lower performance across all image quality metrics. Follow-Your-Canvas performs comparably to AnimateDiff in perspective image quality metrics (IQ, AQ); while it generates more aesthetically pleasing details, it may suffer from distortion in projected perspective views. All methods achieve similar scores in perspective motion quality metrics (MS, SC). For the 360° motion metric EPE, video-conditioned generation methods outperform text- or image-based approaches. 360DVD achieves better EPE scores than AnimateDiff, as it leverages ground-truth optical flow as input.

We also involve a user study to examine the generated 360 ◦ videos. We invite 26 users with expertise in video and 3D generation to assess the results across three dimensions: graphics quality, structure plausibility, and temporal coherence in both 360 videos and projected perspective views. Tab. 1 reports the average user ranking of all four methods, and our method achieves the best performance in all three dimensions. Please refer to the supplementary material for more details regarding the setup and metrics. Our method also achieves superior panorama image outpainting performance, and we provide the comparison in the supplementary material.

4.3 Qualitative comparisons

We present the qualitative comparison between Imagine360 and other baseline models in Fig. 4. At each frame, we present 4 projected perspective views ( ϕ = 0 ◦ , θ = [0 ◦ , 90 ◦ , 180 ◦ , 270 ◦ ] ) to examine the panoramic structure plausibility. Fig. 4 shows that AnimateDiff+LatentLab360 and Follow-Your-Canvas fail to achieve 360 ◦ close-loop continuity (orange box) and they produce mild-

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scale motion, as observed in the change of the canoe location (left-side) and animal size (right-side). 360DVD produces more distorted patterns and blurred visual appearance in both cases. In contrast, our Imagine360 achieves superior visual quality, and plausible, obvious motion in the generated 360 ◦ videos. We also provide side-by-side comparison videos in the supplements for clearer visualization.

4.4 Efficiency Analysis

We provide a runtime breakdown table (Tab. 5) and efficiency comparison table (Tab. 4) in terms of runtime and VRAM consumption. We report the average runtime based on tests using 16-frame video clips.

From the efficiency comparison, we observe that compared to another V2V model Follow-YourCanvas (FYC), our model is faster in inference speed and consumes less VRAM. The main reason is that FYC requires multiple rounds of outpainting to ensure outpaint quality, and they also train and infer in float32. Thanks to our dual-branch structure, Imagine360 achieves good visual quality in a single pass of inference. AnimateDiff and 360DVD have faster inference speeds, which is within expectation, as they are text-guided and image-guided frameworks. In addition, by employing multiple memory optimization techniques (bfloat16,vae slicing,cuda.empty_cache, intermediate tensor cleanup, etc.), our dual-branch structure does not increase VRAM usage by a lot.

In the runtime breakdown table, we observe that the external camera estimation module takes up a considerable amount of time. Although optimizing the inference speed of off-the-shelf camera pose estimator is beyond the scope of this work, we will seek to upgrade this module with a faster and more robust camera estimator.

4.5 Ablative studies

Ablation on dual-branch design. Fig. 5 shows that with limited training data, single panorama branch often proposes non-spherical patterns, and single perspective branch without global guidance produces messy boundary artifacts. Quantitative results in Tab. 2 also support the effectiveness of the dual-branch design. We also study the dual-branch fine-tuning strategy (see supp.), and find that compared to fine-tuning the pano motion module, adding motion LoRA is insufficient to generate good spherical pattern.

Figure 7: Qualitative ablation on the antipodal mask shows improved reversed motion in the antipodal view for forward-moving camera motion. Best viewed with Acrobat Reader for the motion.

Ablation on rotation-aware designs. As shown in Fig. 6, neglecting camera rotations in the input anchor video introduces artifacts in the scene geometry of the resulting 360 ◦ video. Without rotationaware processing, the mountain appears to grow unnaturally taller (see yellow box). In contrast, our rotation-aware design preserves consistent scene geometry. We provide a clearer video comparison in supp. to show the robustness to diverse rotations. Quantitative results in Tab. 2 also validates the benefits of rotation-aware designs.

Ablation on antipodal mask. Fig. 7 illustrates the impact of the antipodal mask on antipodal motion. Thanks to the antipodal mask, as the camera moves forward in the input direction (center view), we can observe clear backward motion in the antipodal views (see red box). Quantitative result in Tab. 2 also proves the effectiveness of adding antipodal activations for general videos. We show the backward view for both cases: the result without antipodal mask exhibits distorted patterns at faraway locations, while with antipodal mask, the camera moves away from the scene in a more plausible way. We further verify that this design does not compromise content quality for rotation-only videos. On a filtered subset of 100 such videos from the test set, VBench IQ and AQ scores remain comparable: with the antipodal mask, IQ is 0.6688 and AQ is 0.5553; without it, IQ is 0.6621 and AQ is 0.5649 .

Analysis on camera estimation model. In our default setting, we use MonST3R for camera pose estimation during inference, which provides accurate and stable results in most cases. While precise pose estimation remains challenging in rare corner cases, this is beyond our paper's scope. Nevertheless, as camera pose estimation continues to advance, MonST3R can be seamlessly replaced with more powerful models. For instance, we replace MonST3R with more advanced TTT3R [3] and report the results in Tab. 3. Results with TTT3R improve on vbench IQ, AQ, and SC metrics, indicating more stable pose estimation ability of TTT3R over MonST3R.

Analysis on backbone model. As more advanced video generation backbone emerges, we also upgrade our backbone model from AnimateDiff [10] to Wan [29] with technical modifications from Unet to DiT. We find that this upgrade significantly improves the image quality and temporal consistency, especially solving the temporal flickering issue observed in some of our primary results. We report the results with Wan [29] in Tab. 3, and show qualitative results in the webpage as well as in the supplementary.

5 Conclusion

In this paper, we propose Imagine360, the first framework for video-conditioned 360 video generation with structured 360 motion. Our key contributions are: (1) a dual-branch video denoising network with panorama and perspective branches for global-local constraints; (2) an antipodal mask in crossdomain spherical attention to capture reverse translational camera motion; and (3) rotation-aware designs to handle diverse camera poses. We also introduce YouTube360 , a high-quality 360 video dataset with diverse, structured foreground motion. Experiments show that Imagine360 achieves superior video quality and panoramic motion realism.

Limitations and future work. During inference, Imagine360 leverages camera pose estimation model to obtain the input camera poses. While these methods [44; 3] generally performs well, it can underestimate rapid movements involving large rotations due to data bias, leading to noticeable distortions (see supp.) that limit the video fidelity in certain scenarios. As research in geometry estimation continues to progress, we plan to keep our model updated with more advanced techniques to alleviate this issue.

Acknowledgments and Disclosure of Funding

This work is supported by Shanghai Artificial Intelligence Laboratory.

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Answer: [Yes]

Justification: LLM is used exclusively for writing, editing, and data processing. See the method section and supplementary material.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel method, Imagine360, to lift standard perspective video into 360° video. The authors first design a dual-branch denoising U-Net equipped with cross-domain spherical attention to capture both global context and local details. Additionally, they introduce an antipodal mask to model long-range motion dependencies. To handle diverse perspective video inputs, a rotation-aware data sampling strategy is proposed. Experimental results demonstrate the effectiveness of the proposed approach.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.\tThe paper is well-written and easy to follow.\n\n2.\tThe use of a dual-branch U-Net to capture both global and local correlations is an interesting and effective design choice.\n\n3.\tThe demo is engaging and clearly demonstrates the practical applications of the proposed method.\n\nWeaknesses:\n\n1.\tThe visual quality of the generated videos is relatively poor. Although the proposed method achieves state-of-the-art performance compared to the baselines, the results still exhibit noticeable artifacts, such as blurring and temporal inconsistencies between adjacent frames. Given that recent video generation models (e.g., CogVideo and Wan-2.1) are capable of producing higher-quality outputs, it would be better if the authors could further explain these limitations.\n\n2.\tIt is unclear whether the baseline models were trained using the same dataset collected by the authors. If not, the comparisons may not be entirely fair, and this should be clarified.\n\n3.\tThe proposed method involves a relatively complex pipeline. It would be helpful if the authors provided a comparison of training and inference costs to better assess the model’s efficiency and practical applicability.\n\nQUESTIONS:\nPlease refer to the Strengths And Weaknesses section.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper develops a pipeline for generating panoramic videos from a monocular perspective video. Specifically, the input perspective video is projected onto a panoramic canvas, where unobserved regions are outpainted by a finetuned dual-branch video inpainting model. This method comprises three key designs: the dual-branch video outpainting, antipodal mask, and rotation-aware design. The dual-batch design synergistically outpaints videos in panoramic and perspective domains, ensuring global and local high-quality visual and motion patterns. The antipodal mask is designed to enhance the motion connection of front-and-back regions. To align the pose changes of the input perspective video, the rotation-aware design is employed to augment the training data. Finally, experimental results demonstrate the superior performance of this proposed method over existing video outpainting and text2pano video generation methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n(1) This paper develops a pipeline that can convert an input monocular perspective video into a panoramic 360 video.\n(2) Antipodal mask explicitly connects regions with motion relevance.\n(3) This paper is well-organized and easy to follow.\n\nWeaknesses:\n(1) The technique contribution is limited. This paper is a simple combination of [1-3] and additionally adds a perspective video as input. As shown in Table 2, the most significant design is the dual-branch denoising outpainting, which has been proposed by [1].  And the rotation-aware design is essentially camera poses augmentation.\n\n(2) The antimodal mask design needs further verification. In Table 2, using the antimodal mask design brings minor improvements. Although Fig. 7 demonstrates the efficacy of the antimodal mask, only one case cannot exhibit the generalization capability. Moreover, I notice that the video in supp (a white SUV driving down a dirt road in a forested mountain valley) also has the problem of strange motion patterns (around the image boundary), which do not yield inverse motion effects. I suggest providing two perspective views, including both the input video and its antipodal views, for better comparisons.\n \n[1] Taming stable diffusion for text to 360 panorama image generation\n[2] 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model\n[3] AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning\n\nQUESTIONS:\n(1) Since the MonST3R is employed to estimate the camera poses during inference, why not directly use this model to predict camera trajectories from enormous monocular videos, and use these predicted camera poses to replace the rotation-aware design during training? By doing this, we can reduce the domain gap, and the predicted camera poses are more realistic. \n\n(2) Please clarify the contributions of this paper.\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Imagine360, a new method for generating 360° panoramic videos from standard perspective video inputs. The core contributions include a dual-branch denoising architecture (for perspective and panoramic views), an antipodal attention mask to model spherical motion consistency, and rotation-aware designs to handle diverse camera poses. The authors also introduce a new curated dataset, YouTube360, consisting of 10K high-quality 360° video clips. Extensive experiments (including comparisons against SOTA methods and ablation studies) demonstrate clear improvements in both perceptual quality and motion coherence.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n- The task of perspective-to-360° video generation is both new and practically relevant. The paper clearly motivates the gap between current methods (which rely on panoramic guidance) and the proposed task. \n- The dual-branch video denoising framework, along with cross-domain attention and the antipodal mask, is a clever adaptation of recent generative modeling ideas to the spherical video domain. The use of antipodal attention for capturing global motion dependencies is well-motivated. The proposed rotation-aware training and inference makes the proposed method more robust on general inputs\n- The experimental results are extensive. In Table 1, the proposed method outperforms all baselines across nearly every metric. The ablations in Table 2 show that each proposed module contributes meaningfully to the performance of the full method. Qualitative comparisons are also provided to show the improvement of the proposed method.\n\n- New dataset. This paper introduces a new dataset YouTube360, which addresses the training data bottleneck in this domain. This dataset is a valuable resource, especially given the lack of clean, high-quality 360° training videos for generative models.\n\n### Weaknesses\n\n- The dual-branch design mostly follows PanFusion, which makes the contribution of this part a bit weak.\n- The dual-branch architecture and rotation-aware modules likely add computational overhead. A discussion or table showing runtime comparisons or inference speed would be valuable for understanding the practical trade-offs.\n- While the new dataset is helpful for training, it remains unclear how the model performs on in-the-wild or low-quality user-generated videos. A discussion or stress test would make the paper’s robustness claims more convincing.\n\nQUESTIONS:\nPlease refer to the questions in Weaknesses. In addtion, \n\n- Somehow I think the generated videos are flickering if looking closely, a discussion on why this happens and how this could be improved would be beneficial\n\n- Why does this paper choose MonST3R for camera pose estimation? How robust it is and what if using more robust models like MegaSAM etc?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel framework that converts standard videos into immersive 360° content. Key innovations include a dual-branch denoising architecture with spherical attention, an antipodal mask for motion modeling, and rotation-aware processing. Besides, They also introduce a new 360 videos dataset. Experiments demonstrate superior performance over baselines in metrics and user studies, with ablations validating each component.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The proposed task of perspective-to-360° video generation is new, bridging accessibility with high-end content creation for immersive media. The method demonstrates generalization to in-the-wild videos, significantly boosting its practical utility.\n- Using a cross-domain spherical attention to model reversed motion patterns sounds new and is proved effective by ablations.\n- The rotation-aware modules for in-the-wild videos with different camera poses makes sense and enhances the generation robustness from looking at ablations.\n- Data contribution: provides high-quality 360 video datasets with highlight time intervals to the community.\n\nWeaknesses:\n- While inference speed is mentioned, no clear comparison to baseline efficiency or resource requirements is available.\n- Missing details of metric computation: EPE is originally computed in depth estimation. How is it used here to evaluate motion quality? Also, how exactly is the OmniFVD extended from OmniFID here?\n- The [41] and [42] both refer to the same citation in the reference section\n\nQUESTIONS:\nQuestions:\n- When MonST3R’s camera pose estimation fails or is inaccurate, does it manifest as noticeable artifacts? Can you discuss possible solutions and illustrate the robustness of the system?\n- How does the inference speed compare to baseline efficiency and resource requirements?\n- Missing details of metric computation: EPE is originally computed in depth estimation. How is it used here to evaluate motion quality? Also, how exactly is the OmniFVD extended from OmniFID here?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}