提出STCN模型，实现更自然的3D人体运动随机预测。
A Spatio-temporal Continuous Network for Stochastic 3D Human Motion Prediction
- 用时空连续网络生成平滑运动序列，引入锚点集防止模式坍缩。
- 在Human3.6M和HumanEva-I上同时提升预测多样性和准确率。
- 适合关注运动多样性与真实感生成的研究者或开发者。
由于广泛应用，随机人体运动预测（HMP）受到越来越多关注。尽管生成领域进展迅速，现有方法在学习连续时间动态和预测随机运动序列方面仍面临挑战，常忽略复杂人体动作的灵活性，易发生模式坍缩。为此，我们提出一种名为STCN的新方法，用于随机且连续的人体运动预测，包含两个阶段：第一阶段，设计时空连续网络生成更平滑的运动序列，并创新性地将锚点集引入随机HMP任务，以表征潜在的人体运动模式，防止模式坍缩；第二阶段，利用锚点集获取观测运动序列的高斯混合分布（GMM），关注每个锚点的概率，并通过从每个锚点采样多个序列来缓解同类运动内部差异。在两个常用数据集（Human3.6M 和 HumanEva-I）上的实验结果表明，该模型在多样性与准确性方面均表现优异。
原文摘要 · Abstract (English)
Stochastic Human Motion Prediction (HMP) has received increasing attention due to its wide applications. Despite the rapid progress in generative fields, existing methods often face challenges in learning continuous temporal dynamics and predicting stochastic motion sequences. They tend to overlook the flexibility inherent in complex human motions and are prone to mode collapse. To alleviate these issues, we propose a novel method called STCN, for stochastic and continuous human motion prediction, which consists of two stages. Specifically, in the first stage, we propose a spatio-temporal continuous network to generate smoother human motion sequences. In addition, the anchor set is innovatively introduced into the stochastic HMP task to prevent mode collapse, which refers to the potential human motion patterns. In the second stage, STCN endeavors to acquire the Gaussian mixture distribution (GMM) of observed motion sequences with the aid of the anchor set. It also focuses on the probability associated with each anchor, and employs the strategy of sampling multiple sequences from each anchor to alleviate intra-class differences in human motions. Experimental results on two widely-used datasets (Human3.6M and HumanEva-I) demonstrate that our model obtains competitive performance on both diversity and accuracy.
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