# Arbitrary Motion Style Transfer via Contrastive Learning
## 4 Experiments
### Ablation Study  
**Cycle loss**. Compared to the method proposed by [PERSON] et al. [4], our model shows a significant decrease in both CC and SC scores, as presented in Table 1. We attribute this improvement to the use of cycle loss, which enhances the model's ability to retain both the content and style of the input motion during style transfer. Unlike the method of [PERSON] et al. [4], which directly uses reconstruction loss to enhance the model's ability to retain the content of the motion, our approach employs cycle loss that leverages the cycle consistency in style transfer [10]. This allows style transfer to operate without relying on paired data, thereby deepening and broadening the model's perception of the style transfer task. In Table 2, our model, which incorporates reconstruction loss and cyclic consistency loss, achieved a higher CRA than [PERSON] et al. [6]. This improvement may be due to the fact that the 1D convolution is more suitable for smaller datasets like [PERSON] et al. [28].  
**Contrastive loss**. After incorporating contrastive learning for both generation motion and input motion, there is a noticeable decrease in the CC and SC scores. This indicates that the model effectively learns the differences between various motion styles through  
Figure 4: Content interpolation. Input content: Childlike-kicking. Input style: Sexy-kicking. \(\alpha=1.0\) represents reconstruction of input style motion.  
Figure 5: Style interpolation. Input content: Old-walking. Input style: Proud-walking. \(\beta=0.0\) represents reconstruction of input content motion.  
contrastive learning, enabling it to accurately identify and retain the content and stylistic details of the input motion in the generated motions. In Table 2, the model that incorporates contrastive learning shows an increase in CRA but a decrease in SRA. Considering the decline in CC, this suggests that the enhancement in content retention capability due to contrastive learning enables the model to generate content that more closely exhibits the characteristics of the corresponding content labels.  
**Temporal contrastive loss.** In the experimental group that incorporated temporal contrastive learning, the SC score showed a significant decrease, while the CC score experienced a slight increase. We speculate that this is because the content features are closely related to temporal characteristics, whereas style features can be viewed as second-order statistics of the content features, which are less influenced by time. As a result, the more granular temporal contrastive learning may have caused the model to lose some overall control over the content, leading to a decrease in its ability to retain content. However, the impact on style was minimal, as style is not directly associated with the temporal dimension. Instead, this finer-grained contrastive learning allows the model to decompose the input motion into smaller clips, enabling it to explicitly learn the stylistic characteristics of each part. This approach creates a broader latent space for style features within the model. In Table 2, the model that incorporates temporal contrastive learning shows increases in both CRA and SRA. Considering the decline in SC, this indicates that the enhancement in style retention capability through temporal contrastive learning improves the generation of motions, allowing them to exhibit some characteristics corresponding to the style labels.