论文标题
通过学习互动和几何驱动的密钥帧将人类动画放入3D场景中
Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes
论文作者
论文摘要
我们提出了一种新颖的方法，可以将3D人类动画放入3D场景，同时保持动画中的任何人类娱乐互动。我们使用计算动画中最重要的网格的概念，以与场景的互动，我们称之为“关键帧”。这些关键框架使我们能够更好地优化动画在场景中的位置，从而使动画（站立，铺设，坐着等）中的互动与场景的负担相匹配（例如，站在地板上或躺在床上）。我们将我们称为PAAK的方法与先前的方法进行了比较，包括POSA，Prox地面真相和运动合成方法，并通过感知研究强调了我们方法的好处。人类评估者更喜欢我们的PAAK方法，而不是Prox地面真相数据64.6 \％。此外，在直接比较中，与POSA相比，评估者比包括61.5％的竞争方法更喜欢PAAK。
We present a novel method for placing a 3D human animation into a 3D scene while maintaining any human-scene interactions in the animation. We use the notion of computing the most important meshes in the animation for the interaction with the scene, which we call "keyframes." These keyframes allow us to better optimize the placement of the animation into the scene such that interactions in the animations (standing, laying, sitting, etc.) match the affordances of the scene (e.g., standing on the floor or laying in a bed). We compare our method, which we call PAAK, with prior approaches, including POSA, PROX ground truth, and a motion synthesis method, and highlight the benefits of our method with a perceptual study. Human raters preferred our PAAK method over the PROX ground truth data 64.6\% of the time. Additionally, in direct comparisons, the raters preferred PAAK over competing methods including 61.5\% compared to POSA.
