PVWander: 3D Memory-guided Panoramic Video Wandering with Path-Facing Canonicalization

Long-horizon panoramic video wandering aims to continuously synthesize scene-consistent $360^{\circ }$ observations along user-specified camera trajectories from an initial equirectangular projection (ERP) panorama. Existing panoramic video models have achieved promising results in camera-controlled generation, while recent 3D-guided approaches further provide geometric guidance for improving scene consistency. However, stable long-horizon wandering remains challenging due to diverse motion–orientation relationships and imperfect memory guidance induced by accumulated generation artifacts and depth inaccuracies. To address these challenges, we propose **PVWander**, a 3D-memory-guided framework that accumulates generated observations into an online point-cloud memory and reprojects it to guide subsequent generation, with two complementary designs: *Path-Facing canonicalization* for motion–orientation alignment and *Timestep-aware gating modulation* for adaptive memory guidance. *Path-Facing canonicalization* exploits the horizontal cyclic symmetry of ERP panoramas to uniformly align each clip's ERP orientation with its principal translation direction, yielding more consistent, forward-like relative motions while preserving the prescribed camera path. *Timestep-aware gating modulation* exploits the coarse-to-fine nature of diffusion denoising to adaptively regulate reprojected memory guidance through timestep-dependent channel-wise gates, preserving its structural benefits while mitigating visual artifacts from imperfect projections. Extensive experiments demonstrate that PVWander achieves improved long-horizon generation quality, while ablations validate the effectiveness of both designs. Code will be available.