MoRAG -- Multi-Fusion Retrieval Augmented Generation for Human Motion (2409.12140v2)

Abstract: We introduce MoRAG, a novel multi-part fusion based retrieval-augmented generation strategy for text-based human motion generation. The method enhances motion diffusion models by leveraging additional knowledge obtained through an improved motion retrieval process. By effectively prompting LLMs, we address spelling errors and rephrasing issues in motion retrieval. Our approach utilizes a multi-part retrieval strategy to improve the generalizability of motion retrieval across the language space. We create diverse samples through the spatial composition of the retrieved motions. Furthermore, by utilizing low-level, part-specific motion information, we can construct motion samples for unseen text descriptions. Our experiments demonstrate that our framework can serve as a plug-and-play module, improving the performance of motion diffusion models. Code, pretrained models and sample videos are available at: https://motion-rag.github.io/

Overview of MoRAG - Multi-Fusion Retrieval Augmented Generation for Human Motion

Kalakonda et al. present MoRAG, a multi-part fusion-based retrieval augmented generation framework for text-based human motion generation. The primary innovation of this framework lies in its utilization of part-specific motion retrieval models integrated with LLMs, which notably enhances motion diffusion models, especially in generating high-quality sequences from complex or unseen text descriptions.

Methodology

The proposed MoRAG framework includes several interconnected components:

1. Part-Specific Motion Descriptions: By leveraging LLMs, the framework generates text descriptions of specific body parts involved in the motion (e.g., torso, hands, legs), which forms the foundation for more precise retrieval tasks.
2. Modified Retrieval-Augmented Generation: The system employs independent motion retrieval models trained for different body parts to enhance the robustness and diversity of retrieved motion sequences. These models utilize a contrastive training objective, filtered through robust text embedding techniques, accommodating variations in text descriptions such as spelling errors or synonyms.
3. Multi-Part Fusion Strategy: The retrieved part-specific motions are fused to construct comprehensive full-body motion sequences. This fusion accounts for spatial coherence, ensuring that the composed motions align semantically with the input text.
4. Incorporation into Diffusion Models: By integrating the constructed motion sequences within a diffusion-based motion generation pipeline, MoRAG ensures that the generated motions exhibit enhanced alignment with the provided text descriptions and improved diversity. This is achieved through a Semantics-Modulated Transformer (SMT) that modulates the denoising process using the additional part-specific motion information.

Experimental Results

The researchers conducted extensive experiments on the HumanML3D dataset, demonstrating significant improvements in generalization, diversity, and zero-shot performance of motion generations. The framework was benchmarked against several state-of-the-art methods, including TMR++, MotionDiffuse, and ReMoDiffuse.

Quantitative Metrics:

• R-Precision: MoRAG achieved competitive scores close to the highest-ranking models, indicating strong semantic relevance of the generated sequences.
• FID (Fréchet Inception Distance): Although not the lowest, the FID score of MoRAG was still favorable, indicating reasonable quality in the generated sequences.
• Diversity and MultiModality: There was a notable improvement in diversity and multimodality, suggesting that the method can generate varied motion sequences even from unseen text descriptions, increasing its utility in diverse applications.

Implications

Practical Applications:

• Enhanced Motion Generation: The ability to generate high-quality, semantically accurate, and diverse human motion sequences from text opens up significant possibilities in animation, virtual reality, gaming, and human-computer interaction domains.

Theoretical Contributions:

• Novel Use of LLMs in Motion Retrieval: The integration of LLMs for part-specific descriptions and the subsequent retrieval process represents an innovative approach that can bridge gaps in existing text-to-motion retrieval systems.
• Improved Generalization: The framework's ability to handle complex and unseen text descriptions effectively highlights the potential for broader application, setting a new standard for robustness in motion generation tasks.

Future Directions

Future research could explore extending MoRAG to other generative architectures beyond diffusion models, ensuring broader applicability. Additionally, incorporating finer granularity in part-specific databases, such as finger, head, and lip movements, could further enhance the realism and complexity of generated sequences. Another worthwhile avenue could be the creation of synthetic training data, aiding in the development of more robust and diverse motion generation models.

In conclusion, the MoRAG framework, with its innovative multi-part fusion and retrieval augmented generation strategy, presents a meaningful advancement in the field of human motion generation. This approach not only strengthens the generalizability and diversity of generated motions but also sets a precedent for incorporating advanced LLMs into motion retrieval and generation pipelines.