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Imagine360: Immersive 360 Video Generation from Perspective Anchor
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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 user-friendly 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 extended web $360^\circ$ 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 elevation-aware designs that adapt to varying video masking due to changing elevations across frames. Extensive experiments show Imagine360 achieves superior graphics quality and motion coherence among state-of-the-art $360^\circ$ video generation methods. We believe Imagine360 holds promise for advancing personalized, immersive $360^\circ$ video creation.
Forward citations
Cited by 5 Pith papers
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ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models
A panorama representation and attention scheme that lets a pretrained perspective video diffusion model generate spatially consistent 360-degree videos from an input perspective clip.
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Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment
Leader360V provides a 10,000+ video, 198-class, densely annotated 360-degree video dataset with an LLM-assisted automatic annotation pipeline, and shows fine-tuning on it improves 360 video segmentation and tracking models.
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PanoWan: Lifting Diffusion Video Generation Models to 360{\deg} with Latitude/Longitude-aware Mechanisms
PanoWan adapts the Wan 2.1 text-to-video model to generate seamless 360-degree videos by remapping initial noise, rotating the latent grid during denoising, and padding the latent before VAE decoding, trained on a new...
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Gimbal360: Canonicalizing Planar Diffusion for Spherical Panorama Completion
Gimbal360 completes 360° panoramas from unposed perspective images by rigidly auto-leveling inputs and training diffusion with a Siamese shift-equivariance loss to preserve ERP seam continuity.
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PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation
Fine-tuning a pretrained video diffusion model with LoRA rank 16 on about 1,000 synthetic videos produces panoramic video with good seam closure, but the claim that rank must exceed 8 degrees of freedom is not proven.
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