While existing motion style transfer methods are effective between two
motions with identical content, their performance significantly diminishes when
transferring style between motions with different contents. This challenge lies
in the lack of clear separation between content and style of a motion. To
tackle this challenge, we propose a novel motion style transformer that
effectively disentangles style from content and generates a plausible motion
with transferred style from a source motion. Our distinctive approach to
achieving the goal of disentanglement is twofold: (1) a new architecture for
motion style transformer with 'part-attentive style modulator across body
parts' and 'Siamese encoders that encode style and content features
separately'; (2) style disentanglement loss. Our method outperforms existing
methods and demonstrates exceptionally high quality, particularly in motion
pairs with different contents, without the need for heuristic post-processing.
Codes are available at this https URL

基于新的模型架构和风格解耦损失，我们提出了一种有效解决在不同内容的运动之间进行风格传递的问题的运动风格转换器，它能够将风格与内容有效分离，并生成具有传入风格的逼真运动。我们的方法在各方面表现优于现有方法，在不同内容的运动对中表现出特别高的质量，而无需启发式后处理。

MoST：多种动作内容之间的动作风格转换网络

MoST: Motion Style Transformer between Diverse Action Contents

Siamese encoders such as sentence transformers are among the least understood
deep models. Established attribution methods cannot tackle this model class
since it compares two inputs rather than processing a single one. To address
this gap, we have recently proposed an attribution method specifically for
Siamese encoders (M\"oller et al., 2023). However, it requires models to be
adjusted and fine-tuned and therefore cannot be directly applied to
off-the-shelf models. In this work, we reassess these restrictions and propose
(i) a model with exact attribution ability that retains the original model's
predictive performance and (ii) a way to compute approximate attributions for
off-the-shelf models. We extensively compare approximate and exact attributions
and use them to analyze the models' attendance to different linguistic aspects.
We gain insights into which syntactic roles Siamese transformers attend to,
confirm that they mostly ignore negation, explore how they judge semantically
opposite adjectives, and find that they exhibit lexical bias.

对 Siamese encoders 进行了方法验证，分析了其对语言学不同方面的关注，以及其词汇偏好。

现成孪生 transformer 网络的近似归属度

Approximate Attributions for Off-the-Shelf Siamese Transformers

Recent contrastive based 3D action representation learning has made great
progress. However, the strict positive/negative constraint is yet to be relaxed
and the use of non-self positive is yet to be explored. In this paper, a
Contrastive Positive Mining (CPM) framework is proposed for unsupervised
skeleton 3D action representation learning. The CPM identifies non-self
positives in a contextual queue to boost learning. Specifically, the siamese
encoders are adopted and trained to match the similarity distributions of the
augmented instances in reference to all instances in the contextual queue. By
identifying the non-self positive instances in the queue, a positive-enhanced
learning strategy is proposed to leverage the knowledge of mined positives to
boost the robustness of the learned latent space against intra-class and
inter-class diversity. Experimental results have shown that the proposed CPM is
effective and outperforms the existing state-of-the-art unsupervised methods on
the challenging NTU and PKU-MMD datasets.

本文提出了一种基于对比学习的骨骼三维动作表示学习的非自身正样本挖掘（CPM）框架，采用孪生编码器训练匹配所有实例的相似度分布，通过在上下文队列中识别非自身正实例，提出了一种增强正实例学习策略，从而提高学习的鲁棒性，并在 NTU 和 PKU-MMD 数据集上取得了优异的效果。