FlashCHORD: Reusing and Reinforcing Distillation for Efficient 4D Scene Generation

Recent advances in distilling 4D scene motion from video generation models have demonstrated promising results. However, lengthy optimization and motion artifacts limit their practical use. In this paper, we introduce FlashCHORD, a framework that generates more realistic motion with substantially shorter optimization times. At the core of our method is a strategy to retain and reuse supervision signals obtained from expensive video generation model evaluations. Our analysis further shows that optimizing deformation through matching rendered appearance often leads to motion artifacts, as the resulting motion is highly sensitive to the input appearance. We address this with semantics-aware coloring, which recolors scene regions to provide visual cues better suited to motion optimization. Together, these designs yield a framework that outperforms prior distillation-based methods in both runtime and motion quality. Extensive experiments demonstrate its effectiveness across diverse scenes and motion prompts. Video results are available at https://chordflash.github.io/chordflashpage/.