论文标题
Action2Motion：3D人类动作的条件产生
Action2Motion: Conditioned Generation of 3D Human Motions
论文作者
论文摘要
动作识别是一个相对确定的任务，在这种任务中，人类运动的输入序列是预测其交流类别。另一方面，本文考虑了一个相对的问题，可以将其视为行动识别的倒数：鉴于规定的动作类型，我们旨在在3D中生成可见的人类运动序列。重要的是，预计一组生成的动作将使其植物保持探索整个动作条件的运动空间。同时，每个采样序列都忠实地类似于Anaturhuman体型动力学。在这些目标的推动下，我们通过采用谎言代数来代表当时的自然动作，遵循人类运动学的物理法。我们还提出了鼓励运动空间的广告采样的临时变异自动编码器（VAE）。还构建了一个新的3D人类运动数据集人物12。经验实验的三个不同的人类运动数据集（包括我们的）证明了我们方法的有效性。
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category. This paper, on the other hand, considers a relativelynew problem, which could be thought of as an inverse of actionrecognition: given a prescribed action type, we aim to generateplausible human motion sequences in 3D. Importantly, the set ofgenerated motions are expected to maintain itsdiversityto be ableto explore the entire action-conditioned motion space; meanwhile,each sampled sequence faithfully resembles anaturalhuman bodyarticulation dynamics. Motivated by these objectives, we followthe physics law of human kinematics by adopting the Lie Algebratheory to represent thenaturalhuman motions; we also propose atemporal Variational Auto-Encoder (VAE) that encourages adiversesampling of the motion space. A new 3D human motion dataset, HumanAct12, is also constructed. Empirical experiments overthree distinct human motion datasets (including ours) demonstratethe effectiveness of our approach.
