We introduce a method to synthesize animator guided human motion across 3D scenes. Given a set of sparse (3 or 4) joint locations (such as the location of a person's hand and two feet) and a seed motion sequence in a 3D scene, our method generates a plausible motion sequence starting from the seed motion while satisfying the constraints imposed by the provided keypoints. We decompose the continual motion synthesis problem into walking along paths and transitioning in and out of the actions specified by the keypoints, which enables long generation of motions that satisfy scene constraints without explicitly incorporating scene information. Our method is trained only using scene agnostic mocap data. As a result, our approach is deployable across 3D scenes with various geometries. For achieving plausible continual motion synthesis without drift, our key contribution is to generate motion in a goal-centric canonical coordinate frame where the next immediate target is situated at the origin. Our model can generate long sequences of diverse actions such as grabbing, sitting and leaning chained together in arbitrary order, demonstrated on scenes of varying geometry: HPS, Replica, Matterport, ScanNet and scenes represented using NeRFs. Several experiments demonstrate that our method outperforms existing methods that navigate paths in 3D scenes.

翻译：我们提出了一种方法，用于在3D场景中合成受动画师引导的人体运动。给定一组稀疏（3或4个）关节位置（例如人手和双脚的位置）以及3D场景中的一段种子运动序列，我们的方法能够生成从种子运动开始、同时满足关键点约束的合理运动序列。我们将持续性运动合成问题分解为沿路径行走以及根据关键点定义的动作进行进入与切换，从而在不显式引入场景信息的情况下，生成长时段满足场景约束的运动。我们的方法仅使用与场景无关的动作捕捉数据进行训练，因此可部署于具有不同几何形状的3D场景。为实现无漂移的合理持续性运动合成，我们的关键贡献在于：在目标中心化的规范坐标系中生成运动，其中下一个即时目标位于原点。我们的模型能够生成长序列的多样化动作（如抓取、坐下、倚靠），这些动作可按任意顺序组合，并在多种几何场景（包括HPS、Replica、Matterport、ScanNet以及基于NeRF表示的场景）中得以验证。多项实验表明，我们的方法优于现有在3D场景中进行路径导航的方法。