DynamicScaler: Seamless and Scalable Video Generation for Panoramic
  Scenes
The increasing demand for immersive AR/VR applications and spatial
intelligence has heightened the need to generate high-quality scene-level and
360{\deg} panoramic video. However, most video diffusion models are constrained
by limited resolution and aspect ratio, which restricts their applicability to
scene-level dynamic content synthesis. In this work, we propose the
DynamicScaler, addressing these challenges by enabling spatially scalable and
panoramic dynamic scene synthesis that preserves coherence across panoramic
scenes of arbitrary size. Specifically, we introduce a Offset Shifting
Denoiser, facilitating efficient, synchronous, and coherent denoising panoramic
dynamic scenes via a diffusion model with fixed resolution through a seamless
rotating Window, which ensures seamless boundary transitions and consistency
across the entire panoramic space, accommodating varying resolutions and aspect
ratios. Additionally, we employ a Global Motion Guidance mechanism to ensure
both local detail fidelity and global motion continuity. Extensive experiments
demonstrate our method achieves superior content and motion quality in
panoramic scene-level video generation, offering a training-free, efficient,
and scalable solution for immersive dynamic scene creation with constant VRAM
consumption regardless of the output video resolution. Our project page is
available at https://dynamic-scaler.pages.dev/.
67617735a0a946bd33fd708c
Jinxiu Liu
67617735a0a946bd33fd708d
Shaoheng Lin
67617735a0a946bd33fd708e
Yinxiao Li
67617735a0a946bd33fd708f
Ming-Hsuan Yang
DynamicScaler: Seamless and Scalable Video Generation for Panoramic
  Scenes
The increasing demand for immersive AR/VR applications and spatial
intelligence has heightened the need to generate high-quality scene-level and
360{\deg} panoramic video. However, most video diffusion models are constrained
by limited resolution and aspect ratio, which restricts their applicability to
scene-level dynamic content synthesis. In this work, we propose the
DynamicScaler, addressing these challenges by enabling spatially scalable and
panoramic dynamic scene synthesis that preserves coherence across panoramic
scenes of arbitrary size. Specifically, we introduce a Offset Shifting
Denoiser, facilitating efficient, synchronous, and coherent denoising panoramic
dynamic scenes via a diffusion model with fixed resolution through a seamless
rotating Window, which ensures seamless boundary transitions and consistency
across the entire panoramic space, accommodating varying resolutions and aspect
ratios. Additionally, we employ a Global Motion Guidance mechanism to ensure
both local detail fidelity and global motion continuity. Extensive experiments
demonstrate our method achieves superior content and motion quality in
panoramic scene-level video generation, offering a training-free, efficient,
and scalable solution for immersive dynamic scene creation with constant VRAM
consumption regardless of the output video resolution. Our project page is
available at https://dynamic-scaler.pages.dev/.
DynamicScaler enables high-quality panoramic dynamic scene synthesis with resolution scalability and motion coherence using a rotating Window and Global Motion Guidance.
video diffusion models spatially scalable panoramic dynamic scene synthesis Offset Shifting Denoiser diffusion model seamless rotating Window Global Motion Guidance content quality motion continuity constant VRAM consumption