HMVLM:Human Motion-Vision-Language Model via MoE LoRA

Lei Hu, Yongjing Ye, Shihong Xia

Keywords: Multimodal, Motion synthesis, Pose Estimation, Foundation Model

TL;DR: We propose a Human Motion-Vision-Language Model (HMVLM) based on MoE LoRA for diverse human-centric downstream tasks. A novel "zero expert" is introduced to mitigate catastrophic forgetting during the instruction-tuning.

Abstract: The expansion of instruction-tuning data has enabled foundation language models to exhibit improved instruction adherence and superior performance across diverse downstream tasks. Semantically-rich 3D human motion is being progressively integrated with these foundation models to enhance multimodal understanding and cross-modal generation capabilities. However, the modality gap between human motion and text raises unresolved concerns about catastrophic forgetting during this integration. In addition, developing autoregressive-compatible pose representations that preserve generalizability across heterogeneous downstream tasks remains a critical technical barrier. To address these issues, we propose the Human Motion-Vision-Language Model (HMVLM), a unified framework based on the Mixture of Expert Low-Rank Adaption(MoE LoRA) strategy. The framework leverages the gating network to dynamically allocate LoRA expert weights based on the input prompt, enabling synchronized fine-tuning of multiple tasks. To mitigate catastrophic forgetting during instruction-tuning, we introduce a novel zero expert that preserves the pre-trained parameters for general linguistic tasks. For pose representation, we implement body-part-specific tokenization by partitioning the human body into different joint groups, enhancing the spatial resolution of the representation. Experiments show that our method effectively alleviates knowledge forgetting during instruction-tuning and achieves remarkable performance across diverse human motion downstream tasks.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 3528