基于约束扩散生成的可扩展运动风格迁移
Scalable Motion Style Transfer with Constrained Diffusion Generation
AI总结：
针对现有运动风格迁移依赖跨域一致性损失、可扩展性差的问题，提出带有关键帧流形约束梯度的约束扩散生成框架，实现多域独立训练，在10种舞蹈风格迁移的内容保留上显著优于基线，主观评估也验证了其竞争力。
摘要：
当前运动风格迁移系统的训练依赖跨风格域的一致性损失来保留内容，这阻碍了其在大量域及私有数据上的可扩展应用。近期的图像迁移研究表明，利用扩散模型间的隐式桥接，可在每个域上独立训练，但内容保留能力仅限于简单数据模式。我们通过在反向扩散过程中施加有偏采样，同时保持训练阶段的域独立性来解决这一问题。我们从源域关键帧构建偏差，并将其作为内容约束的梯度应用，由此得到带有关键帧流形约束梯度（KMCGs）的框架。我们的验证表明，训练独立模型以在多达10种舞蹈运动风格间进行迁移是可行的。综合实验发现，与基线模型和基于扩散的消融风格迁移模型相比，该方法在运动内容保留方面有显著提升。此外，我们开展了一项人类研究，对生成的舞蹈运动质量进行主观评估，结果验证了KMCGs的竞争力。
英文摘要：
Current training of motion style transfer systems relies on consistency losses across style domains to preserve contents, hindering its scalable application to a large number of domains and private data. Recent image transfer works show the potential of independent training on each domain by leveraging implicit bridging between diffusion models, with the content preservation, however, limited to simple data patterns. We address this by imposing biased sampling in backward diffusion while maintaining the domain independence in the training stage. We construct the bias from the source domain keyframes and apply them as the gradient of content constraints, yielding a framework with keyframe manifold constraint gradients (KMCGs). Our validation demonstrates the success of training separate models to transfer between as many as ten dance motion styles. Comprehensive experiments find a significant improvement in preserving motion contents in comparison to baseline and ablative diffusion-based style transfer models. In addition, we perform a human study for a subjective assessment of the quality of generated dance motions. The results validate the competitiveness of KMCGs.
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