# Arbitrary Motion Style Transfer via Contrastive Learning
## 4 Experiments
### Comparison
Additionally, we constructed a dataset that completely removes samples with the \"childlike\" style label and the \"punching\" content label for training the model, aimed at testing its generalization capability. As shown in Table 1, there is a slight increase in CC, while SC shows a significant increase. This indicates that the model can structurally analyze the content of the input motions and retain relevant details without a significant \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **CC \(\downarrow\)** & **SC \(\downarrow\)** \\ \hline [PERSON] et al. [4] & \(33.91\pm 6.33\) & \(35.34\pm 6.45\) \\ [PERSON] et al. [6] & \(10.05\pm 4.39\) & \(9.83\pm 4.20\) \\ \hline Ours \(\text{w}/\mathcal{L}=\mathcal{L}_{\text{rec}}+\mathcal{L}_{\text{cyc}}+ \mathcal{L}_{\text{pos}}\) & \(9.37\pm 3.99\) & \(10.11\pm 3.6\) \\ Ours \(\text{w}/\mathcal{L}=\mathcal{L}_{\text{rec}}+\mathcal{L}_{\text{cyc}}+ \mathcal{L}_{\text{pos}}+\mathcal{L}_{\text{contra}}\) & \(\boldsymbol{9.01\pm 4.00}\) & \(9.68\pm 3.59\) \\ Ours \(\text{w}/\mathcal{L}=\mathcal{L}_{\text{rec}}+\mathcal{L}_{\text{cyc}}+ \mathcal{L}_{\text{pos}}+\mathcal{L}_{\text{temp-contra}}\) & \(10.05\pm 4.11\) & \(\boldsymbol{9.19\pm 3.15}\) \\ Ours (limited) & \(9.27\pm 4.41\) & \(10.18\pm 4.12\) \\ Ours & \(9.20\pm 4.05\) & \(9.34\pm 3.54\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Content Consistency (CC) and Style Consistency (SC) to evaluate the performance of our method against other approaches, including results from ablation studies. The symbol “\(\downarrow\)” signifies that a lower value is better; the best-performing results are highlighted in bold. The label “(Limited)” denotes that a specific style and content category have been completely removed from the training dataset to assess the model’s generalization performance. drop in performance due to unseen labels. However, the model's ability to preserve style details demonstrates a noticeable degradation. We speculate that this may be partly due to the inherent difficulty in learning styles, combined with the limited variety of style data, which may not provide sufficient generalization capability. Additionally, our use of temporal contrastive learning may have slightly increased the model's tendency to overfit to existing style. Secondly, we utilized the spatio-temporal convolution-based recognition model proposed by [PERSON] et al. [39] to train a baseline recognition model on the dataset used in this paper, aimed at monitoring the quality of generated motions. Content Recognition Accuracy (CRA) refers to the accuracy of the generated motions assessed by using a baseline recognition model as a classifier. It measures how effectively the generated motions align with their corresponding content labels. Style Recognition Accuracy (SRA), on the other hand, measures the accuracy of the generated motions in terms of their stylistic alignment. As shown in Table 2, our global style transfer contrastive learning group achieved the highest CRA. However, our final model exhibited the second highest SRA, with a noticeable gap compared to the model proposed by [PERSON] et al. [6]. For content input, our model effectively retained content-related details. However, for style input, our model struggled to accurately generate the corresponding styles. We believe this can be attributed to two main factors: first, the inherent difficulty of learning styles, which requires a wide variety of styles to provide sufficient information; our training dataset (1.5k clips) is significantly smaller than the one used by [PERSON] et al. [6] (120k clips). Second, we did not employ the skeleton-based graph convolution used by [PERSON] et al. [6], which resulted in our model lacking an understanding of the spatial structure of the data. This understanding is crucial for recognition, as the baseline model also relies on spatio-temporal convolution. Overall, these factors contribute to the significant performance gap.