AtlasFlow: Seam-Aware Sampling and Perspective Distillation for Panorama Generation

An equirectangular panorama is stored with an arbitrary longitude cut, and planar diffusion priors attach meaning to it: released ERP generators break there, and their samples change when the cut moves. We remove this dependence without updating the panorama backbone. AtlasFlow evaluates the frozen denoiser in two opposite longitude charts, aligns the predicted velocities, and fuses them with a symmetric partition of unity that downweights each chart near its cut. The sampler is exactly half-turn equivariant in latent space; co-rotating the chart layout extends this to every integer roll, randomized per sample at no extra cost. On Matterport3D the stored-cut error of matched DiT360 falls by 31% at no cost in aggregate quality: the complete method leads eight of ten reference and quality metrics among twelve generators and improves eight over DiT360; on ODI-SR1200 it reduces FID from 59.64 to 55.02 and FAED from 16.82 to 14.64. An 18.1K-parameter Perspective Velocity Adapter (PVA), a periodic residual distilled from the denoiser’s own square-view predictions with no teacher at inference, composes with the sampler and supplies its reference-quality and viewport gains. We further show that frame-boundary seam metrics miss a relocated seam and depend strongly on decoding: a lossless half-turn roll passes them, whereas a yaw-invariant prominence read-out detects injected off-boundary seams with AUC 0.92 where the boundary metric stays at chance.