Text-driven Human Motion Generation with Motion Masked Diffusion Model

TL;DR

This paper introduces the Motion Masked Diffusion Model (MMDM), a novel approach that enhances text-driven human motion generation by explicitly learning spatio-temporal relationships through masking strategies, resulting in improved motion quality and consistency.

Contribution

The paper proposes a new motion masked mechanism for diffusion models, with two masking strategies, to better learn spatio-temporal relations in human motion generation.

Findings

Effective balance of motion quality and text-motion consistency.

Improved FID scores on HumanML3D and KIT-ML datasets.

Enhanced ability to learn spatio-temporal relationships.

Abstract

Text-driven human motion generation is a multimodal task that synthesizes human motion sequences conditioned on natural language. It requires the model to satisfy textual descriptions under varying conditional inputs, while generating plausible and realistic human actions with high diversity. Existing diffusion model-based approaches have outstanding performance in the diversity and multimodality of generation. However, compared to autoregressive methods that train motion encoders before inference, diffusion methods lack in fitting the distribution of human motion features which leads to an unsatisfactory FID score. One insight is that the diffusion model lack the ability to learn the motion relations among spatio-temporal semantics through contextual reasoning. To solve this issue, in this paper, we proposed Motion Masked Diffusion Model \textbf{(MMDM)}, a novel human motion masked mechanism for diffusion model to explicitly enhance its ability to learn the spatio-temporal relationships from contextual joints among motion sequences. Besides, considering the complexity of human motion data with dynamic temporal characteristics and spatial structure, we designed two mask modeling strategies: \textbf{time frames mask} and \textbf{body parts mask}. During training, MMDM masks certain tokens in the motion embedding space. Then, the diffusion decoder is designed to learn the whole motion sequence from masked embedding in each sampling step, this allows the model to recover a complete sequence from incomplete representations. Experiments on HumanML3D and KIT-ML dataset demonstrate that our mask strategy is effective by balancing motion quality and text-motion consistency.