Human action-anticipation methods predict what is the future action by
observing only a few portion of an action in progress. This is critical for
applications where computers have to react to human actions as early as
possible such as autonomous driving, human-robotic interaction, assistive
robotics among others. In this paper, we present a method for human action
anticipation by predicting the most plausible future human motion. We represent
human motion using Dynamic Images and make use of tailored loss functions to
encourage a generative model to produce accurate future motion prediction. Our
method outperforms the currently best performing action-anticipation methods by
4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.

本文提出了一种使用动态图像和定制损失函数的生成模型来预测人类行为的方法，该方法在三个基准测试中相较于现有的最佳方法增强了 4% 至 5.2% 的预测准确性。

通过预测未来动态图像来预测动作

Action Anticipation By Predicting Future Dynamic Images

This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI), for both isolated and continuous action recognition. These dynamic
images are constructed from a segmented sequence of depth maps using
hierarchical bidirectional rank pooling to effectively capture the
spatial-temporal information. Specifically, DDI exploits the dynamics of
postures over time and DDNI and DDMNI exploit the 3D structural information
captured by depth maps. Upon the proposed representations, a ConvNet based
method is developed for action recognition. The image-based representations
enable us to fine-tune the existing Convolutional Neural Network (ConvNet)
models trained on image data without training a large number of parameters from
scratch. The proposed method achieved the state-of-art results on three large
datasets, namely, the Large-scale Continuous Gesture Recognition Dataset (means
Jaccard index 0.4109), the Large-scale Isolated Gesture Recognition Dataset
(59.21%), and the NTU RGB+D Dataset (87.08% cross-subject and 84.22%
cross-view) even though only the depth modality was used.

本论文提出了三种简单、紧凑而有效的深度序列表示，称为 DDI、DDNI 和 DDMNI，用于孤立和连续行为识别。在这些动态图像上，设计了基于 ConvNet 的方法进行行动识别，取得了三个大型数据集上的最先进结果，即 Large-scale Continuous Gesture Recognition Dataset（Jaccard 指数 0.4109）、Large-scale Isolated Gesture Recognition Dataset（59.21％）和 NTU RGB + D Dataset（跨主体 87.08％和跨视角 84.22％），即使只使用深度模态。