在潜在空间中通过运动扩散执行你的命令
Executing your Commands via Motion Diffusion in Latent Space
AI总结：
针对条件人体运动生成任务，提出基于运动潜在空间的扩散模型MLD，通过VAE提取低维潜在表示并在其上执行扩散，显著提升了生成质量并大幅降低了计算开销。
摘要：
我们研究了一项具有挑战性的任务，即条件人体运动生成，该任务根据各种条件输入（如动作类别或文本描述符）生成合理的人体运动序列。由于人体运动高度多样化，并且具有与条件模态（如自然语言中的文本描述符）截然不同的分布特性，因此很难学习从所需条件模态到人体运动序列的概率映射。此外，来自动作捕捉系统的原始运动数据可能在序列中存在冗余并包含噪声；直接对原始运动序列和条件模态的联合分布进行建模将需要沉重的计算开销，并可能导致由捕捉噪声引入的伪影。为了学习各种人体运动序列的更好表示，我们首先设计了一个强大的变分自编码器（VAE），并为人体运动序列得出了一个具有代表性且低维的潜在代码。然后，我们不使用扩散模型来建立原始运动序列和条件输入之间的连接，而是在运动潜在空间上执行扩散过程。我们提出的基于运动潜在空间的扩散模型（MLD）能够生成符合给定条件输入的生动运动序列，并在训练和推理阶段都大幅减少了计算开销。在各种人体运动生成任务上的大量实验表明，我们的MLD在广泛的人体运动生成任务中相比最先进的方法取得了显著改进，并且比以往在原始运动序列上的扩散模型快两个数量级。
英文摘要：
We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities, such as textual descriptors in natural languages, it is hard to learn a probabilistic mapping from the desired conditional modality to the human motion sequences. Besides, the raw motion data from the motion capture system might be redundant in sequences and contain noises; directly modeling the joint distribution over the raw motion sequences and conditional modalities would need a heavy computational overhead and might result in artifacts introduced by the captured noises. To learn a better representation of the various human motion sequences, we first design a powerful Variational AutoEncoder (VAE) and arrive at a representative and low-dimensional latent code for a human motion sequence. Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space. Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences.
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。
