Synthesizing physically plausible human motions in 3D scenes is a challenging
problem. Kinematics-based methods cannot avoid inherent artifacts (e.g.,
penetration and foot skating) due to the lack of physical constraints.
Meanwhile, existing physics-based methods cannot generalize to multi-object
scenarios since the policy trained with reinforcement learning has limited
modeling capacity. In this work, we present a framework that enables physically
simulated characters to perform long-term interaction tasks in diverse,
cluttered, and unseen scenes. The key idea is to decompose human-scene
interactions into two fundamental processes, Interacting and Navigating, which
motivates us to construct two reusable Controller, i.e., InterCon and NavCon.
Specifically, InterCon contains two complementary policies that enable
characters to enter and leave the interacting state (e.g., sitting on a chair
and getting up). To generate interaction with objects at different places, we
further design NavCon, a trajectory following policy, to keep characters'
locomotion in the free space of 3D scenes. Benefiting from the divide and
conquer strategy, we can train the policies in simple environments and
generalize to complex multi-object scenes. Experimental results demonstrate
that our framework can synthesize physically plausible long-term human motions
in complex 3D scenes. Code will be publicly released at
this https URL

构建了一个框架，通过分解人 - 场景交互为相互作用和导航两个基本过程，实现了在复杂的三维场景中进行长期交互任务的真实物理模拟角色。