FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control
This work addresses the problem of versatile video generation control for AI and multimedia applications, offering a scalable solution that is not incremental but introduces a new paradigm.
The paper tackles the challenge of generalizable control in video generation by proposing FlexAM, a framework that disentangles appearance and motion using a novel 3D control signal represented as a point cloud, achieving superior performance across tasks like I2V/V2V editing, camera control, and spatial object editing.
Effective and generalizable control in video generation remains a significant challenge. While many methods rely on ambiguous or task-specific signals, we argue that a fundamental disentanglement of "appearance" and "motion" provides a more robust and scalable pathway. We propose FlexAM, a unified framework built upon a novel 3D control signal. This signal represents video dynamics as a point cloud, introducing three key enhancements: multi-frequency positional encoding to distinguish fine-grained motion, depth-aware positional encoding, and a flexible control signal for balancing precision and generative quality. This representation allows FlexAM to effectively disentangle appearance and motion, enabling a wide range of tasks including I2V/V2V editing, camera control, and spatial object editing. Extensive experiments demonstrate that FlexAM achieves superior performance across all evaluated tasks.