MotionLab: Unified Human Motion Generation and Editing via the
  Motion-Condition-Motion Paradigm
Authors: name: Ziyan Guo, name: Zeyu Hu, name: Na Zhao, name: De Wen Soh

Human motion generation and editing are key components of computer graphics
and vision. However, current approaches in this field tend to offer isolated
solutions tailored to specific tasks, which can be inefficient and impractical
for real-world applications. While some efforts have aimed to unify
motion-related tasks, these methods simply use different modalities as
conditions to guide motion generation. Consequently, they lack editing
capabilities, fine-grained control, and fail to facilitate knowledge sharing
across tasks. To address these limitations and provide a versatile, unified
framework capable of handling both human motion generation and editing, we
introduce a novel paradigm: Motion-Condition-Motion, which enables the unified
formulation of diverse tasks with three concepts: source motion, condition, and
target motion. Based on this paradigm, we propose a unified framework,
MotionLab, which incorporates rectified flows to learn the mapping from source
motion to target motion, guided by the specified conditions. In MotionLab, we
introduce the 1) MotionFlow Transformer to enhance conditional generation and
editing without task-specific modules; 2) Aligned Rotational Position Encoding}
to guarantee the time synchronization between source motion and target motion;
3) Task Specified Instruction Modulation; and 4) Motion Curriculum Learning for
effective multi-task learning and knowledge sharing across tasks. Notably, our
MotionLab demonstrates promising generalization capabilities and inference
efficiency across multiple benchmarks for human motion. Our code and additional
video results are available at: https://diouo.github.io/motionlab.github.io/.