title: DynamicScaler: Seamless and Scalable Video Generation for Panoramic
    Scenes
summary: 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/.
paper_authors._id: 67617735a0a946bd33fd708c
paper_authors.name: Jinxiu Liu
paper_authors._id: 67617735a0a946bd33fd708d
paper_authors.name: Shaoheng Lin
paper_authors._id: 67617735a0a946bd33fd708e
paper_authors.name: Yinxiao Li
paper_authors._id: 67617735a0a946bd33fd708f
paper_authors.name: Ming-Hsuan Yang
paper_title: DynamicScaler: Seamless and Scalable Video Generation for Panoramic
    Scenes
paper_summary: 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/.
paper_ai_keywords: DynamicScaler spatially scalable panoramic dynamic scene synthesis Offset Shifting Denoiser diffusion model rotating Window seamless boundary transitions Global Motion Guidance content quality motion quality training-free VRAM consumption