Synthesizing Physically Plausible Human Motions in 3D Scenes (2308.09036v1)

Abstract: 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 https://github.com/liangpan99/InterScene.

Insights into Synthesizing Physically Plausible Human Motions in 3D Scenes

The paper "Synthesizing Physically Plausible Human Motions in 3D Scenes" addresses a critical challenge in computer vision and graphics, which is the generation of realistic human motions interacting dynamically within cluttered 3D environments. It positions itself at the intersection of kinematic modeling and physics-based simulations, offering novel insights into enhancing the interaction realism of virtual characters with their surroundings.

This research critically observes that while kinematics-based methodologies can create varied motions, they commonly suffer from physical artifacts due to their lack of physical realism constraints, such as foot skating and object penetration issues, which are prevalent in existing implementations. Conversely, traditional physics-driven approaches often falter in generalizing across multiple-object scenarios due to limited reinforcement learning policy capacities.

The key contribution of the framework introduced herein revolves around a nuanced decomposition of human-scene interactions into two foundational processes: Interacting and Navigating. This decomposition is operationalized by crafting two distinct but reusable controllers: Interaction Controller (InterCon) and Navigation Controller (NavCon). InterCon, comprising sit and get-up policies, facilitates seamless engagement and disengagement of characters with objects. Contrastingly, NavCon is engineered to govern characters' locomotion along trajectory paths that circumvent obstacles, ensuring motion realism even in complex multi-object scenes.

The experimental results underscore the framework's capacity to synthesize physically plausible long-term motions, corroborated by strong numerical metrics detailed in their results. For instance, the success rates achieved in the sitting tasks reveal a significant performance uplift, as indicated by a higher comparative success rate and precision relative to control baselines and other models. Moreover, the successful generalization of these control policies to complex 3D environments without necessitating additional training highlights the efficacy of the modular decomposition approach.

Speculative forward-thinking prompts several intriguing avenues for future research and practical application in AI and simulation environments. Firstly, expanding the repository of interaction controllers could accommodate a wider spectrum of human-object interaction scenarios—spanning beyond sitting and getting up to perhaps more sophisticated motions like leaning or carrying dynamic objects. Such expansion would inherently augment the adaptability and utility of virtual human agents across varied platforms such as augmented reality, gaming, or virtual training simulations.

Additionally, addressing current limitations in dynamic obstacle avoidance as evidenced by subtle navigational kinematics issues aligns with broader AI research goals focusing on real-time decision-making within fluid, unpredictable environments—a perennial topic in autonomous systems research.

In summary, this paper offers a meticulous, technically rich approach to improving the realism and context-awareness of virtual character motility in 3D scenes. Its contributions lie not only in the innovative architectural dichotomy enabling efficient cross-object interaction modeling but also in its systemic roadmap for future advancements in physics-integrated interactive simulations. Through rigorous empirical validation, it sets a compelling precedent for subsequent research in physically-based interactive motion synthesis.