FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control

Effective and generalizable control in video generation re-mains a significant challenge. While many methods rely on ambiguousor task-specific signals, we argue that a fundamental disentanglement of"appearance" and "motion" provides a more robust and scalable path-way. We propose FlexAM, a unified framework built upon a novel 3Dcontrol signal. This signal represents video dynamics as a point cloud,introducing three key enhancements: multi-frequency positional encodingto distinguish fine-grained motion, depth-aware encoding, and a flexiblecontrol signal for balancing precision and generalization. This represen-tation allows FlexAM to effectively disentangle appearance and motion,enabling a wide range of tasks including I2V/V2V editing, camera con-trol, and spatial object editing. Extensive experiments demonstrate thatFlexAM achieves superior performance across all evaluated tasks. Codesare available at https://github.com/IGL-HKUST/FlexAM.