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

2026-02-13 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

FlexAM is a novel framework designed to enhance control in video generation by fundamentally disentangling "appearance" and "motion." It introduces a unified approach built upon a new 3D control signal that represents video dynamics as a point cloud. This signal incorporates three key enhancements: multi-frequency positional encoding for distinguishing fine-grained motion, depth-aware positional encoding, and a flexible control signal to balance precision and generative quality. This disentanglement enables FlexAM to perform a wide array of video generation tasks, including image-to-video (I2V) and video-to-video (V2V) editing, camera control, and spatial object editing. Extensive experiments confirm FlexAM's superior performance across these evaluated tasks.

Key takeaway

For research scientists developing advanced video generation models, FlexAM's approach to disentangling appearance and motion offers a robust pathway to more versatile and controllable systems. You should explore integrating 3D point cloud representations and enhanced positional encodings into your next-generation video synthesis architectures to achieve superior performance across diverse editing tasks.

Key insights

Disentangling appearance and motion via a 3D point cloud control signal improves video generation control.

Principles

• Fundamental disentanglement improves scalability.
• 3D control signals can represent video dynamics.
• Positional encoding enhances motion distinction.

Method

FlexAM uses a 3D point cloud control signal with multi-frequency and depth-aware positional encoding, plus a flexible control signal, to disentangle appearance and motion for versatile video generation.

In practice

• Apply to I2V/V2V editing.
• Utilize for camera path control.
• Enable spatial object manipulation.

Topics

• Video Generation Control
• Appearance-Motion Disentanglement
• 3D Control Signal
• Positional Encoding
• I2V/V2V Editing

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.