EchoMask: Speech-Queried Attention-based Mask Modeling for Holistic Co-Speech Motion Generation

Abstract

Masked modeling framework has shown promise in co-speech motion generation. However, it struggles to identify semantically significant frames for effective motion masking. In this work, we propose a speech-queried attention-based mask modeling framework for co-speech motion generation. Our key insight is to leverage motion-aligned speech features to guide the masked motion modeling process, selectively masking rhythm-related and semantically expressive motion frames. Specifically, we first propose a motion-audio alignment module (MAM) to construct a latent motion-audio joint space. In this space, both low-level and high-level speech features are projected, enabling motion-aligned speech representation using learnable speech queries. Then, a speech-queried attention mechanism (SQA) is introduced to compute frame-level attention scores through interactions between motion keys and speech queries, guiding selective masking toward motion frames with high attention scores. Finally, the motion-aligned speech features are also injected into the generation network to facilitate co-speech motion generation. Qualitative and quantitative evaluations confirm that our method outperforms existing state-of-the-art approaches, successfully producing high-quality co-speech motion. The code will be released at https://github.com/Xiangyue-Zhang/EchoMask.

Demo

Masking Problem

The main question is where masking should happen. Random masking is blind, and loss-based masking can over-focus on hard but uninformative transitions. EchoMask uses speech as the query signal.

Method

EchoMask first aligns audio and motion in a shared latent space, then uses speech-queried attention to decide which motion frames should be masked and reconstructed.

Qualitative Results

The examples show that speech-guided masking improves both body gestures and facial articulation, especially where semantic cues should trigger clearer motion.

Quantitative Results

Lower is better for FGD, MSE, and LVD; higher is better for BC and DIV. EchoMask gives the best holistic score on most metrics while remaining competitive in diversity.

BibTeX

@inproceedings{zhang2025echomask,
  title={EchoMask: Speech-Queried Attention-based Mask Modeling for Holistic Co-Speech Motion Generation},
  author={Zhang, Xiangyue and Li, Jianfang and Zhang, Jiaxu and Ren, Jianqiang and Bo, Liefeng and Tu, Zhigang},
  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
  pages={10827--10836},
  year={2025}
}