生成式补间：3D人体运动的长期补间
Generative Tweening: Long-term Inbetweening of 3D Human Motions
摘要：
数十年来，在遵循特定艺术约束的同时，大规模生成复杂且逼真的人体动画的能力一直是游戏和动画行业的基本目标。流行的技术包括关键帧，基于物理的模拟，以及通过运动图的数据库方法。最近，已经引入了基于深度学习的运动生成器。虽然这些学习模型可以自动生成任意长度的高度复杂的程式化动作，但它们仍然缺乏用户控制。为此，我们引入了长期的中间插入，这涉及到自动合成复杂的运动在很长的时间间隔非常稀疏的关键帧由用户的问题。我们确定了一些与此问题相关的挑战，包括保持生物力学和关键帧约束，保留自然运动，并在考虑所有约束的同时整体设计整个运动序列。我们引入了一个生物力学约束的生成对抗网络，它以关键帧约束为条件，对人体运动进行长期的中间插入。该网络使用一种新颖的两阶段方法，首先以关节角度的形式预测局部运动，然后预测全局运动，即角色遵循的全局路径。由于通常有许多可能的运动可以满足给定的用户约束，我们还使我们的网络能够使用我们称为Motion DNA的方案生成各种输出。这种方法允许用户通过向网络提供种子运动（DNA）来操纵和影响输出内容。通过使用79类捕获的运动数据进行训练，我们的网络在各种高度复杂的运动风格上表现出色。
英文摘要：
The ability to generate complex and realistic human body animations at scale, while following specific artistic constraints, has been a fundamental goal for the game and animation industry for decades. Popular techniques include key-framing, physics-based simulation, and database methods via motion graphs. Recently, motion generators based on deep learning have been introduced. Although these learning models can automatically generate highly intricate stylized motions of arbitrary length, they still lack user control. To this end, we introduce the problem of long-term inbetweening, which involves automatically synthesizing complex motions over a long time interval given very sparse keyframes by users. We identify a number of challenges related to this problem, including maintaining biomechanical and keyframe constraints, preserving natural motions, and designing the entire motion sequence holistically while considering all constraints. We introduce a biomechanically constrained generative adversarial network that performs long-term inbetweening of human motions, conditioned on keyframe constraints. This network uses a novel two-stage approach where it first predicts local motion in the form of joint angles, and then predicts global motion, i.e. the global path that the character follows. Since there are typically a number of possible motions that could satisfy the given user constraints, we also enable our network to generate a variety of outputs with a scheme that we call Motion DNA. This approach allows the user to manipulate and influence the output content by feeding seed motions (DNA) to the network. Trained with 79 classes of captured motion data, our network performs robustly on a variety of highly complex motion styles.
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。
