PwP: Panoramas without Panoramas — Camera-Controlled 360° Video from Any View via Perspective-Only Adaptation

360° video generation holds great promise for immersive applications such as virtual reality, yet precise control over camera motion in panoramic videos remains largely unexplored. Existing methods either lack explicit camera control or require a 360° panorama as the starting frame, which is costly to obtain and limits their practical applicability. In this work, we present a diffusion-based framework for camera-controllable 360° video generation that removes the dependence on panoramic start frames. Given a conditioning input and a target camera trajectory, our model generates temporally coherent 360° videos that faithfully follow the specified camera motion. To this end, we introduce a camera conditioning mechanism tailored to the spherical geometry of panoramic video, together with a training strategy that enables the model to synthesize full 360° content without panoramic input. Extensive experiments demonstrate that our method achieves state-of-the-art camera control accuracy while also improving visual quality and spatial consistency across viewpoints compared with existing approaches.