Large Motion Model: A Unified Approach for Multi-Modal Motion Generation

Core Concepts

This work presents the Large Motion Model (LMM), the first generalist multi-modal motion generation model that can perform multiple motion generation tasks simultaneously and achieve competitive performance across a wide range of benchmarks.

Abstract

The paper introduces the Large Motion Model (LMM), a motion-centric, multi-modal framework that unifies mainstream motion generation tasks into a generalist model. To address the challenges of heterogeneous motion data and tasks, the authors make the following key contributions:

MotionVerse: A mega-scale, multi-modal, multi-task motion generation dataset that features a unified motion representation across a wide range of tasks and motion formats.

LMM Architecture: The authors design an articulated attention mechanism ArtAttention that incorporates body part-aware modeling into a Diffusion Transformer backbone, allowing for precise and robust control.

Pre-Training Strategy: The authors propose a novel pre-training strategy for LMM, including random frame rates and various masking techniques, to fully leverage extensive motion datasets and enhance the model's capabilities.

Extensive experiments demonstrate that the generalist LMM achieves competitive performance across various standard motion generation tasks over state-of-the-art specialist models. LMM also exhibits strong generalization capabilities and emerging properties across many unseen tasks. Ablation studies provide valuable insights about training and scaling up large motion models for future research.

Stats

The MotionVerse dataset consolidates 16 datasets with a total of 320k sequences and 100 million frames, spanning 10 tasks.
The unified motion representation divides the human body into 10 independent parts, addressing the inconsistencies in motion formats across datasets.

Quotes

"Leveraging multi-modal and multi-task motion generation datasets presents significant challenges. First, disparate datasets feature varying motion formats and evaluation metrics, such as keypoint-based or rotation-based formats, and metrics assessing realism or diversity."
"To deal with these challenges, we first amass multiple cross-modal motion datasets, encompassing 16 datasets with a total of 320k sequences and 100 million frames. These datasets span seven standard tasks: text-to-motion, action-to-motion, motion prediction, speech-to-gesture, music-to-dance, motion imitation, and motion in-betweening."

Key Insights Distilled From

Large Motion Model for Unified Multi-Modal Motion Generation

by Mingyuan Zha... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01284.pdf

Deeper Inquiries

How can the unified motion representation be further extended to handle missing individual keypoints within a body part?

In order to handle missing individual keypoints within a body part in the unified motion representation, a more flexible approach needs to be implemented. One potential solution could involve incorporating a mechanism that can dynamically adjust the representation based on the availability of keypoints. This could involve creating a hierarchical structure where the representation can adapt to missing keypoints by redistributing the information from neighboring keypoints or utilizing contextual information from other body parts. Additionally, introducing a mechanism for interpolation or extrapolation based on the available keypoints can help in filling the gaps caused by missing data. By enhancing the adaptability and robustness of the representation, the model can better handle scenarios with missing individual keypoints within a body part.

What are the potential risks and ethical considerations in deploying large motion models for practical applications, such as deepfake video generation?

Deploying large motion models for practical applications, especially in scenarios like deepfake video generation, poses several potential risks and ethical considerations. Some of these include:

Misinformation and Manipulation: Large motion models can be used to create highly realistic deepfake videos that can spread misinformation, manipulate public opinion, and deceive individuals.

Privacy Concerns: Generating realistic human motions using large models may infringe on individuals' privacy by creating fake videos without their consent, leading to privacy violations and potential harm.

Identity Theft: Deepfake videos created using large motion models can be used for identity theft, where individuals' faces and actions are manipulated to commit fraudulent activities.

Legal Implications: The use of deepfake videos generated by large motion models can have legal implications, such as defamation, intellectual property theft, and the creation of false evidence in legal proceedings.

Social Impact: The proliferation of deepfake videos can erode trust in media and information sources, leading to social unrest and undermining the credibility of authentic content.

To mitigate these risks, it is essential to implement strict regulations, ethical guidelines, and technological safeguards when deploying large motion models for practical applications like deepfake video generation. Transparency, accountability, and responsible use of such technology are crucial to address the ethical considerations associated with its deployment.

How can the long-sequence motion generation capabilities of LMM be improved to better serve user needs in real-world scenarios?

To enhance the long-sequence motion generation capabilities of LMM for better usability in real-world scenarios, several strategies can be implemented:

Incremental Generation: Implement a mechanism for incremental generation where the model can generate motions in segments and seamlessly concatenate them to form long sequences. This approach can help manage memory constraints and improve the efficiency of generating extended motions.

Temporal Coherence: Enhance the model's ability to maintain temporal coherence and consistency throughout long sequences by incorporating feedback mechanisms or reinforcement learning techniques to ensure smooth transitions between different segments of the motion.

Multi-Modal Integration: Integrate multi-modal inputs more effectively to guide the generation of long sequences, allowing users to provide diverse condition signals that influence the motion generation process over extended periods.

Dynamic Adaptation: Develop adaptive mechanisms that can adjust the motion generation process based on the evolving context or user feedback, enabling the model to respond dynamically to changing requirements and user preferences during the generation of long sequences.

By implementing these enhancements, LMM can better cater to user needs in real-world scenarios by providing more robust, coherent, and customizable long-sequence motion generation capabilities.