CharacterGAN：Few-Shot关键点角色动画和角色定位
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
摘要：
我们介绍了一个生成模型，它只能在给定字符的几个样本（8 - 15）上进行训练。我们的模型基于关键点位置生成新颖的姿势，这些姿势可以在实时的修改，同时提供交互式反馈，从而实现直观的静止和动画。由于我们只有非常有限的训练样本，因此关键挑战之一在于如何解决（dis）遮挡，例如当手在身体后面或前面移动时。为了解决这个问题，我们引入了一种新的分层方法，该方法将输入关键点显式地拆分为独立处理的不同层。这些层代表角色的不同部分，并提供了一个强大的隐式偏差，有助于获得逼真的结果，即使有很强的（不）遮挡。为了结合各个层的特征，我们使用了一种以所有关键点为条件的自适应缩放方法。最后，我们引入了一个面具连接约束，以减少失真的文物发生极端的分布构成在测试时。我们表明，我们的方法优于最近的基线，并为不同的角色创建逼真的动画。我们还表明，我们的模型可以处理离散的状态变化，例如，面向左或右的轮廓，不同的层确实学习了这些层中相应关键点的特定特征，并且当有更多数据可用时，我们的模型可以扩展到更大的数据集。
英文摘要：
We introduce CharacterGAN, a generative model that can be trained on only a few samples (8 - 15) of a given character. Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation. Since we only have very limited training samples, one of the key challenges lies in how to address (dis)occlusions, e.g. when a hand moves behind or in front of a body. To address this, we introduce a novel layering approach which explicitly splits the input keypoints into different layers which are processed independently. These layers represent different parts of the character and provide a strong implicit bias that helps to obtain realistic results even with strong (dis)occlusions. To combine the features of individual layers we use an adaptive scaling approach conditioned on all keypoints. Finally, we introduce a mask connectivity constraint to reduce distortion artifacts that occur with extreme out-of-distribution poses at test time. We show that our approach outperforms recent baselines and creates realistic animations for diverse characters. We also show that our model can handle discrete state changes, for example a profile facing left or right, that the different layers do indeed learn features specific for the respective keypoints in those layers, and that our model scales to larger datasets when more data is available.
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。
