LEAD: Enhancing Human Motion Diffusion with Latent Realignment for Improved Realism and Textual Inversion

المفاهيم الأساسية

LEAD, a novel text-to-motion generation model, leverages latent diffusion and a realignment mechanism to create semantically structured motion latents, improving realism, expressiveness, and enabling textual motion inversion for personalized motion synthesis.

الملخص

• Bibliographic Information: Andreou, N., Wang, X., Abrevaya, V. F., Cani, M. P., Chrysanthou, Y., & Kalogeiton, V. (2024). LEAD: Latent Realignment for Human Motion Diffusion. arXiv preprint arXiv:2410.14508.
• Research Objective: This paper introduces LEAD, a novel text-to-motion generation model that addresses the limitations of existing methods by incorporating a latent realignment scheme to enhance the semantic structure of motion latent spaces.
• Methodology: LEAD builds upon latent diffusion models and employs a projector module trained to align the motion latent space with the semantic space of a language model (CLIP). This realignment facilitates the generation of more realistic and expressive motions from textual descriptions. Additionally, the authors introduce the task of motion textual inversion (MTI), enabling the generation of personalized motions based on a few example movements. The model is evaluated on the HumanML3D and KIT-ML datasets using standard text-to-motion generation metrics, including FID, R-precision, MMdist, diversity, and multimodality.
• Key Findings: LEAD demonstrates comparable or superior performance to state-of-the-art methods in text-to-motion generation, exhibiting significant improvements in motion realism (FID) and maintaining strong performance in text-motion consistency (R-precision, MMdist). Qualitative results and user studies confirm that LEAD generates more realistic, expressive, and textually aligned motions compared to baseline methods. Furthermore, LEAD shows promise for motion textual inversion, demonstrating improved capacity in capturing out-of-distribution characteristics.
• Main Conclusions: LEAD's latent realignment mechanism effectively enhances the semantic structure of motion latent spaces, leading to improved realism, expressiveness, and the ability to perform motion textual inversion for personalized motion synthesis. The authors highlight the potential of LEAD for various applications, including 3D content creation, robotics, and virtual reality.
• Significance: This research significantly contributes to the field of text-to-motion generation by introducing a novel approach that addresses the limitations of existing methods. The proposed latent realignment scheme and the introduction of motion textual inversion pave the way for generating more realistic, diverse, and personalized human motions from natural language.
• Limitations and Future Research: While LEAD demonstrates promising results, the authors acknowledge the need for further exploration of motion textual inversion, particularly in capturing fine-grained motion details. Future research could also investigate the application of LEAD to other motion synthesis tasks, such as audio-driven or music-driven motion generation.

الإحصائيات

LEAD achieves a FID score of 0.109 on the HumanML3D dataset, compared to 0.473 for the baseline MLD model, indicating a significant improvement in motion realism.
On the KIT-ML dataset, LEAD achieves a FID score of 0.246, compared to 0.404 for the baseline MLD model, again demonstrating improved realism.
User studies comparing LEAD to MLD and MotionCLIP show that LEAD receives significantly higher ratings for both motion realism and text-motion relevance.
Inference time for LEAD is 0.245 seconds per prompt, compared to 0.236 seconds for MLD, indicating a marginal increase in computational overhead.

اقتباسات

"We hypothesize that a semantically structured motion latent space, i.e. one that inherits some of the rich properties of the language space, can facilitate and improve the task of text-to-motion generation."
"In this work, we propose LEAD, a new text-to-motion model based on latent diffusion [RBL∗22,CJL∗23] that addresses the lack of semantic structure in the latent space."
"Our results show that LEAD achieves on-par performance to the state of the art in terms of motion quality while retaining good performance in terms of diversity and multimodality– a trade-off that none of the competing methods can handle well."

الرؤى الأساسية المستخلصة من

LEAD: Latent Realignment for Human Motion Diffusion

by Nefe... في arxiv.org 10-21-2024

https://arxiv.org/pdf/2410.14508.pdf