论文标题
DLOW：多元化的潜在流动以进行多样化的人类运动预测
DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
论文作者
论文摘要
深层生成模型通常用于人类运动预测，因为它们能够对多模式数据分布进行建模并表征多样化的人类行为。尽管在设计和学习深层生成模型方面已经非常小心，但是在训练之后，如何从深层生成模型中有效地产生不同的样本仍然是一个不足的问题。为了从验证的生成模型中获取样品，大多数现有的生成人类运动预测方法绘制了一组独立的高斯潜在代码，并将其转换为运动样本。显然，这种随机抽样策略不能保证产生不同的样本，原因有两个：（1）独立抽样不能迫使样品多样化； （2）采样仅基于可能性，这可能只会产生与数据分布的主要模式相对应的样品。为了解决这些问题，我们提出了一种新颖的采样方法，即多样化的潜在流（DLOW），以从预告片的深层生成模型中生产出多种样本。与随机（独立的）采样不同，所提出的DLOW采样方法采样单个随机变量，然后用一组可学习的映射功能将其映射到一组相关的潜在代码。然后将相关的潜在代码解码为一组相关样品。在培训期间，DLOW对样本的多样性进行了多样性，作为优化潜在映射以改善样本多样性的目标。先验的设计具有很高的灵活性，可以自定义以产生具有共同特征的多样运动（例如，腿部运动相似，但上身运动多样）。我们的实验表明，在样本多样性和准确性方面，DLOW优于最先进的基线方法。我们的代码在项目页面上发布：https：//www.ye-yuan.com/dlow。
Deep generative models are often used for human motion prediction as they are able to model multi-modal data distributions and characterize diverse human behavior. While much care has been taken into designing and learning deep generative models, how to efficiently produce diverse samples from a deep generative model after it has been trained is still an under-explored problem. To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples. Clearly, this random sampling strategy is not guaranteed to produce diverse samples for two reasons: (1) The independent sampling cannot force the samples to be diverse; (2) The sampling is based solely on likelihood which may only produce samples that correspond to the major modes of the data distribution. To address these problems, we propose a novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model. Unlike random (independent) sampling, the proposed DLow sampling method samples a single random variable and then maps it with a set of learnable mapping functions to a set of correlated latent codes. The correlated latent codes are then decoded into a set of correlated samples. During training, DLow uses a diversity-promoting prior over samples as an objective to optimize the latent mappings to improve sample diversity. The design of the prior is highly flexible and can be customized to generate diverse motions with common features (e.g., similar leg motion but diverse upper-body motion). Our experiments demonstrate that DLow outperforms state-of-the-art baseline methods in terms of sample diversity and accuracy. Our code is released on the project page: https://www.ye-yuan.com/dlow.
