Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs (2311.01015v1)

Abstract: Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. However, these compact text representations may overemphasize the action names at the expense of other important properties and lack fine-grained details to guide the synthesis of subtly distinct motion. In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Such global-to-local structures facilitate a comprehensive understanding of motion description and fine-grained control of motion generation. Correspondingly, to leverage the coarse-to-fine topology of hierarchical semantic graphs, we decompose the text-to-motion diffusion process into three semantic levels, which correspond to capturing the overall motion, local actions, and action specifics. Extensive experiments on two benchmark human motion datasets, including HumanML3D and KIT, with superior performances, justify the efficacy of our method. More encouragingly, by modifying the edge weights of hierarchical semantic graphs, our method can continuously refine the generated motion, which may have a far-reaching impact on the community. Code and pre-training weights are available at https://github.com/jpthu17/GraphMotion.

• The paper introduces hierarchical semantic graphs to enable fine-grained control by decomposing motions into overall movements, actions, and specifics.
• The paper validates GraphMotion on HumanML3D and KIT datasets, achieving superior R-Precision and reduced FID compared to existing methods.
• The paper demonstrates continuous refinement of generated motions, offering enhanced adaptability for applications in gaming, virtual reality, and film.

Fine-Grained Control of Motion Diffusion Models Using Hierarchical Semantic Graphs

The paper "Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs" addresses a crucial challenge in the domain of text-driven human motion generation: achieving precise and nuanced control over generated motion sequences. By introducing hierarchical semantic graphs as a controlling mechanism, the authors present a structured approach to overcoming some of the existing shortcomings in the field, particularly those related to the imbalance of textual representation and the coarseness of motion details.

The authors critique the traditional reliance on sentence-level textual representations for motion generation, highlighting how such compressed representations can disproportionately emphasize action labels while neglecting vital attributes such as direction and intensity. The proposed hierarchical semantic graphs methodically disentangles motion descriptions into three semantic levels: motions, actions, and specifics. These levels underpin a coarse-to-fine diffusion model called GraphMotion, where motion generation is broken down into capturing overall motion first, then individual actions, and finally, specific attributes.

The empirical validation of GraphMotion on benchmark datasets, HumanML3D and KIT, reveals its superiority over state-of-the-art counterparts. Notably, the performance is evaluated using metrics like R-Precision, measuring motion-text alignment, and FID, assessing the realism of generated motions. The results indicate that GraphMotion achieves higher precision in matching text descriptions to motion sequences and surpasses competing methods in generating diverse, realistic, and fine-grained motion.

A standout feature of GraphMotion is its ability to allow continuous refinement of produced motions. By altering the weights assigned to the edges within the hierarchical semantic graphs, users can fine-tune the generated results to align more closely with specific motion dynamics desired, an innovation that promises to expand controllability in motion synthesis applications significantly.

The implications of this work are multifaceted. Practically, the approach enhances the usability and flexibility of motion generation systems in industries like gaming, virtual reality, and film where precise motion dynamics are crucial. Theoretically, it raises the bar for integrating semantic text information into generative models, suggesting pathways for future exploration. For instance, the application of similar semantic graph structures could be extended to other domains of AI requiring fine-grained control, such as scene understanding or robotic manipulation.

Speculation on future directions could involve the interaction of such hierarchical frameworks with LLMs, where the benefits derived from LLMs' comprehensive language understanding could be complemented by the structured, fine-grained control delivered through hierarchical graphs.

In conclusion, "Act As You Wish" makes a commendable contribution by delineating a scalable method for fine-grained motion control, opening avenues for more precise and adaptable motion synthesis technologies, and offering a novel perspective on the intersection of language and motion in AI systems.