Unified Motion Generation and Editing: MotionLab and the Motion-Condition-Motion Paradigm

The generation and editing of human motion is a central component of computer graphics and vision. Applications range from animation in films and video games to robotics and the analysis of human movement patterns. Existing approaches in this field often focus on specific tasks and offer isolated solutions that can be inflexible and inefficient in practice. While there are efforts to unify various motion-related tasks, these methods merely use different modalities as conditions for motion generation. As a result, they lack editing capabilities, fine-grained control, and the ability to share knowledge between tasks.

To overcome these limitations, MotionLab presents itself as a versatile, unified framework that enables both the generation and editing of human motion. At its core is the novel Motion-Condition-Motion paradigm. This paradigm uniformly formulates various tasks based on three concepts: source motion, condition, and target motion. MotionLab utilizes rectified flows to learn the mapping from source motion to target motion, guided by the specified conditions.

Within MotionLab, several innovative components are employed:

The MotionFlow Transformer improves conditional generation and editing without relying on task-specific modules. It allows for flexible adaptation to various scenarios and conditions.

The Aligned Rotational Position Encoding ensures temporal synchronization between source and target motion. This is crucial for realistic and coherent motion sequences.

The Task Specified Instruction Modulation enables precise control of motion generation and editing through specific instructions. This allows the system to be adapted to the respective requirements.

The Motion Curriculum Learning promotes effective multi-tasking learning and knowledge sharing between tasks. The system learns progressively more complex movements and can thus generalize its knowledge efficiently.

MotionLab demonstrates promising generalization capabilities and inference efficiency across multiple human motion benchmarks. The architecture of MotionLab is based on a transformer model, which has been specifically optimized for processing motion data. By using rectified flows, the system can learn complex relationships between movements and use them for the generation and editing of new movements. The integration of Aligned Rotational Position Encoding and Task Specified Instruction Modulation enables precise control and adaptation of the system to various tasks and conditions.

The results of MotionLab demonstrate the power of the Motion-Condition-Motion paradigm and open up new possibilities for the application of AI in motion generation and editing. The ability to generate and edit movements based on other movements offers a high degree of flexibility and control. This could drive the development of new applications in areas such as animation, robotics, and virtual reality.

For developers and researchers involved in the generation and editing of human motion, MotionLab offers a promising platform for the development of innovative applications. The combination of a unified framework, efficient algorithms, and advanced learning methods enables the creation of realistic and complex motion sequences.

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