360Anything: Geometry-Free Lifting of Images and Videos to 360°
Lifting perspective images and videos to 360° panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360° generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything's deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io/.
69732303fb12c92b735b758a
Ziyi Wu
69732303fb12c92b735b758b
Daniel Watson
69732303fb12c92b735b758c
Andrea Tagliasacchi
69732303fb12c92b735b758d
David J. Fleet
69732303fb12c92b735b758e
Marcus A. Brubaker
69732303fb12c92b735b758f
Saurabh Saxena
360Anything: Geometry-Free Lifting of Images and Videos to 360°
Lifting perspective images and videos to 360° panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360° generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything's deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io/.
A geometry-free framework using pre-trained diffusion transformers lifts perspective images and videos to 360° panoramas without requiring camera metadata, achieving state-of-the-art performance through token sequence processing and addressing seam artifacts via circular latent encoding.
diffusion transformers perspective-to-equirectangular mapping token sequences zero-padding VAE encoder circular latent encoding 360° panoramas geometric alignment camera metadata seam artifacts