关键词: 动作纠错生成, 扩散模型, 文本驱动动作编辑, 智能动作指导, 注意力机制

Abstract: Intelligent motion coaching can provide automated and personalized support for sports training, motor skill instruction, and rehabilitation, and therefore has significant practical value. Existing studies mainly focus on Action Quality Assessment (AQA), error identification, or textual feedback, while current motion correction generation methods remain limited in correcting complex continuous motions. To address this issue, this paper proposes an edit-region-guided text-driven motion correction generation method. First, a motion editing selector is constructed to generate an editing prediction signal that characterizes the editing importance of different joints at different time steps based on the learner's original motion and expert textual feedback. Subsequently, the editing prediction signal is combined with joint-level body-region masks to construct edit-region guidance weights, which are then transformed into continuous attention biases and incorporated into a diffusion denoising network. This mechanism enhances the diffusion denoising network to focus more feedback-relevant regions during generation while reducing unnecessary modifications to unrelated motion regions. Comparative experiments are conducted on an expert-feedback paired motion-correction dataset constructed from Ego-Exo4D. The proposed method achieves R@1, R@2, and R@3 scores of 3.13%, 5.76%, and 8.59%, respectively, for Generated-to-Target Retrieval (E2T), with a Median Rank (MedR) of 49. Edit-region Mean Per Joint Position Error (Edit-MPJPE), non-edit-region preservation Mean Per Joint Position Error (Keep-MPJPE), Mean Per Joint Position Error (MPJPE), and Procrustes-Aligned Mean Per Joint Position Error (PA-MPJPE) are 17.06 cm, 21.17 cm, 16.75 cm, and 8.86 cm, respectively. Compared with TMED (Text-based Motion Editing Diffusion), SimMotionEdit (text-based human Motion Editing with motion Similarity prediction), and 3D Pose Based Feedback for Physical Exercises (abbreviated as 3DPBF in this paper), the proposed method achieves the best E2T retrieval performance. Specifically, R@1, R@2, and R@3 improve by 0.81, 0.91, and 1.82 percentage points, respectively, over the second-best method, SimMotionEdit, while MedR improves from 66 to 49. In addition, on the Exercise Correction in 3D (EC3D) dataset, the proposed method achieves a correction success rate of 88.14%, outperforming 3DPBF by 11.87 percentage points. Experimental results demonstrate that the proposed method enables targeted motion correction and achieves favorable correction performance across different motion correction datasets, providing effective support for generating corrective motion demonstrations in intelligent motion coaching systems.

Key words: motion correction generation, diffusion model, text-driven motion editing, intelligent motion coaching, attention mechanism