"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm"

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https://arxiv.org/abs/2502.02358

The paper addresses the challenge of isolated solutions for human motion generation and editing tasks. Current methods lack versatility, fine-grained control and knowledge sharing across tasks.

This paper introduces MotionLab, a unified framework based on the Motion-Condition-Motion paradigm. MotionLab uses rectified flows and a MotionFlow Transformer to map source motion to target motion, guided by conditions. It incorporates Aligned Rotational Position Encoding and Task Instruction Modulation for effective multi-task learning via Motion Curriculum Learning.

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📌 Motion-Condition-Motion paradigm offers a simple yet effective abstraction. It unifies motion generation and editing. Rectified flows enable efficient mapping between source and target motions.

📌 MotionFlow Transformer with Joint Attention and Condition Path is key. It facilitates multi-modal interaction. Adaptive Layer Normalization enhances conditional control without task-specific modules.

📌 Aligned Rotational Position Encoding addresses temporal misalignment. It is crucial for time-sensitive motion tasks. Motion Curriculum Learning enables effective multi-task training.

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Methods Explored in this Paper 🔧:

→ MotionLab uses the Motion-Condition-Motion paradigm. This paradigm unifies motion generation and editing tasks using source motion, condition, and target motion concepts.

→ MotionLab framework is built around the MotionFlow Transformer (MFT). MFT leverages rectified flows to learn the mapping from source motion to target motion based on conditions.

→ MFT includes Joint Attention to enable interaction between tokens from different modalities. A Condition Path differentiates modalities and extracts representations. Aligned Rotational Position Encoding (ROPE) ensures time synchronization.

→ Task Instruction Modulation is used to differentiate tasks by adding a task-specific instruction embedding into the MFT.

→ Motion Curriculum Learning is a training strategy. It organizes tasks by difficulty for effective multi-task learning and knowledge sharing. Training is divided into masked pre-training and supervised fine-tuning stages.

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Key Insights 💡:

→ The Motion-Condition-Motion paradigm effectively unifies diverse human motion generation and editing tasks.

→ MotionLab framework, with its MotionFlow Transformer and Motion Curriculum Learning, achieves versatility and strong performance across various motion tasks.

→ Aligned ROPE is crucial for maintaining temporal synchronization between source and target motions, improving performance in time-sensitive tasks.

→ Task Instruction Modulation and Motion Curriculum Learning are essential for effective multi-task learning and knowledge sharing across different motion tasks.

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Results 📊:

→ MotionLab achieves a FID score of 0.223 in text-based motion generation on the HumanML3D dataset.

→ In trajectory-based motion generation on HumanML3D, MotionLab achieves an average error of 0.0334 when controlling all joints.

→ For text-based motion editing on the MotionFix dataset, MotionLab attains a retrieval rate (R@1) of 56.34%.

→ In trajectory-based motion editing on MotionFix, MotionLab reaches a retrieval rate (R@1) of 72.65%.

→ MotionLab achieves a Style Recognition Accuracy (SRA) of 64.97% in motion style transfer.