AnaMoDiff：基于解耦去噪的二维类比运动扩散
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising
AI总结：
AnaMoDiff提出一种基于解耦去噪的扩散方法，通过潜在光流网络在噪声空间中转移二维运动，实现运动类比与身份保持的最佳权衡。
摘要：
我们提出了AnaMoDiff，一种新颖的基于扩散的方法，用于二维运动类比，该方法应用于铰接角色的原始、未标注视频。我们的目标是将运动从二维驱动视频准确转移到源角色上，同时保持其身份（在外观和自然运动方面）良好，即使源角色和驱动角色在部位比例、运动速度和风格上可能存在显著差异。我们的扩散模型通过一个在噪声潜在空间中运行的潜在光流（LOF）网络来转移输入运动，该网络具有空间感知能力，与原始RGB视频相比处理效率更高，并且即使在密集运动中也能通过扩散去噪过程抵抗伪影。为了实现运动类比和身份保持，我们以特征解耦的方式训练去噪模型，在两个噪声级别上运行。源角色的身份揭示特征通过常规噪声注入学习，而运动特征则通过仅注入大值噪声的LOF扭曲视频学习，前提是涉及姿态和肢体的运动属性由高级特征编码。实验表明，我们的方法在运动类比和身份保持之间取得了最佳权衡。
英文摘要：
We present AnaMoDiff, a novel diffusion-based method for 2D motion analogies that is applied to raw, unannotated videos of articulated characters. Our goal is to accurately transfer motions from a 2D driving video onto a source character, with its identity, in terms of appearance and natural movement, well preserved, even when there may be significant discrepancies between the source and driving characters in their part proportions and movement speed and styles. Our diffusion model transfers the input motion via a latent optical flow (LOF) network operating in a noised latent space, which is spatially aware, efficient to process compared to the original RGB videos, and artifact-resistant through the diffusion denoising process even amid dense movements. To accomplish both motion analogy and identity preservation, we train our denoising model in a feature-disentangled manner, operating at two noise levels. While identity-revealing features of the source are learned via conventional noise injection, motion features are learned from LOF-warped videos by only injecting noise with large values, with the stipulation that motion properties involving pose and limbs are encoded by higher-level features. Experiments demonstrate that our method achieves the best trade-off between motion analogy and identity preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
