# Arbitrary Motion Style Transfer via Contrastive Learning
## 4 Experiments
### Comparison  
We compared our framework with several state-of-the-art motion style transfer methods [4; 6]. All these methods were employed by utilizing publicly available code repositories and default configurations. **Qualitative evaluation. ** Figure 3 shows the qualitative results obtained from our method as well as the two compared methods. Our approach enhances the exaggeration level of the generated motions. In particular, during the transfer of walking motion, our method demonstrates a greater range of torso and limb movements, closely resembling the desired style. Further supplementary video material can be found at [[https://github.com/[PERSON]/Arbitrary-Motion-Style-Transfer-via-Contrastive-Learning](https://github.com/[PERSON]/Arbitrary-Motion-Style-Transfer-via-Contrastive-Learning)]([https://github.com/[PERSON]/Arbitrary-Motion-Style-Transfer-via-Contrastive-Learning](https://github.com/[PERSON]/Arbitrary-Motion-Style-Transfer-via-Contrastive-Learning)), accessed on 10 February 2025. Additionally, our method strives to precisely preserve the content motion while reducing distortions. In the case of jumping motion, the method of [PERSON] et al. [4] yields wired knee joint angle, while the method of [PERSON] et al. [6] prioritizes aligning the kicking motion with the input style rather than the content motion. In the case of jumping motions, only our method correctly captures the direction of the hand, while other results exhibit certain distortions. Figure 3: Comparison between our method, [PERSON] et al. [4], and [PERSON] et al. [6]. This comparison suggests that our approach proves more effective in expressing the input motion style while retaining essential content information. This expressive ability can be attributed to the contrastive learning employed to capture the difference between the generated motion and other motions. By utilizing contrastive learning, our model not only learns to retain the features of the input motion but also captures significant differences between other motions [37]. For style transfer tasks, learning to distinguish the differences between various categories enhances the model's understanding of different styles, allowing it to extract key information from the styles more effectively. Furthermore, by incorporating temporal contrastive learning, the model can decompose the input motion and explicitly compare differences between motion clips at various time scales. This can be viewed from two angles. First, integrating different clips of the same motion allows the model to directly compare these clips and identify their similarities. This helps in extracting essential information for style transfer, which cannot be achieved through direct end-to-end methods. Second, by comparing these randomly selected temporal clips from others, the model's understanding of styles can be enriched. **Quantitative evaluation. ** Firstly, we employed the quantitative evaluation approach outlined in [PERSON] et al. [38], which quantifies Content Consistency (CC) as the Euclidean distance between generated and input content motions. In this context, both the input content motion and style motion share the same style (e.g., both are \"Proud\"), ensuring that the generated motion aligns structurally with the content motion while adopting the target style. Style Consistency (SC) follows a similar methodology, measuring the distance between generated and input style motions when the inputs share the same content. By utilizing these motion comparisons, this approach offers a standardized and reproducible evaluation protocol, effectively assessing the model's ability to preserve both style and content. Table 1 shows the result of CC and SC. [PERSON] et al. [4] shows the highest CC and SC. [PERSON] et al. [6] gives a significant improvement in both of these metrics. Our method further reduces the CC and slightly improves the SC. This result suggests that our method is effectively retaining the content input and produces high-quality stylized motions.