Synthesizing Diverse Human Motions in 3D Indoor Scenes (2305.12411v3)

Abstract: We present a novel method for populating 3D indoor scenes with virtual humans that can navigate in the environment and interact with objects in a realistic manner. Existing approaches rely on training sequences that contain captured human motions and the 3D scenes they interact with. However, such interaction data are costly, difficult to capture, and can hardly cover all plausible human-scene interactions in complex environments. To address these challenges, we propose a reinforcement learning-based approach that enables virtual humans to navigate in 3D scenes and interact with objects realistically and autonomously, driven by learned motion control policies. The motion control policies employ latent motion action spaces, which correspond to realistic motion primitives and are learned from large-scale motion capture data using a powerful generative motion model. For navigation in a 3D environment, we propose a scene-aware policy with novel state and reward designs for collision avoidance. Combined with navigation mesh-based path-finding algorithms to generate intermediate waypoints, our approach enables the synthesis of diverse human motions navigating in 3D indoor scenes and avoiding obstacles. To generate fine-grained human-object interactions, we carefully curate interaction goal guidance using a marker-based body representation and leverage features based on the signed distance field (SDF) to encode human-scene proximity relations. Our method can synthesize realistic and diverse human-object interactions (e.g.,~sitting on a chair and then getting up) even for out-of-distribution test scenarios with different object shapes, orientations, starting body positions, and poses. Experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of both motion naturalness and diversity. Code and video results are available at: https://zkf1997.github.io/DIMOS.

Synthesizing Diverse Human Motions in 3D Indoor Scenes

The advent of immersive virtual environments, whether in video games, AR/VR applications, or simulation models, necessitates realistic human-scene interaction to enhance authenticity and engagement. The paper "Synthesizing Diverse Human Motions in 3D Indoor Scenes" presents a sophisticated reinforcement learning-based framework crafted to generate autonomous and diverse human motions within complex 3D indoor environments. The approach addresses the shortcomings of previous methods, which rely heavily on constrained datasets and often lack the capability for realistic scene interaction.

Overview of Techniques and Contributions

The core contribution of this paper is a robust framework that leverages reinforcement learning (RL) to autonomously synthesize human motions that exhibit a high degree of realism in congested indoor scenes. This involves implementing a motion model developed using a CVAE trained on large-scale motion capture datasets such as AMASS and SAMP. The RL framework incorporates latent variables as action representations, trained via an actor-critic setup utilizing the PPO algorithm. This methodological choice allows the generation of stochastic motion primitives that are continuously updated based on spatial interactions, fostering perpetual and varied human movements.

Critical innovations introduced in this work include:

1. Scene-Aware Policies: Traditional RL motion synthesis forms lack scene awareness, often resulting in unnatural motions such as object penetrations. To address this, the authors propose integrating a 2D binary walkability map within the agent's feature space to allow collision avoidance strategies. This inclusion proves effective, with locomotion policies reliably navigating scenes while evading obstacles.
2. Fine-Grained Control and Proximity Encoding: For human-object interactions, the authors utilize surface body markers as target interaction goals coupled with signed distance fields (SDF) to encode precise human-object proximity. This enables interactions well beyond simple waypoint navigation—think sitting or lying on furniture—executed with a high degree of fidelity.
3. Modular Integration of Navigation Mesh-Based Path Planning: The architects of this framework expand its utility by integrating navigation mesh-based pathfinding algorithms. These augment generation capabilities by automatically producing intermediate waypoints that ensure seamless task execution across differing environments.

Numerical Results and Claims

Empirical evaluations demonstrate the effectiveness of this framework against existing standards such as GAMMA and SAMP. Quantitative metrics related to contact scores, interaction penetration, and scene oblivion underscore a marked improvement in realism and interaction fidelity. The human-scene penetration score, notably lower in this framework, indicates the success in fostering natural interactions, with the model routinely outperforming baselines in perceptual studies.

Implications and Future Directions

The proposed method presents substantial implications for the development of more believable virtual environments. Practically, applications span from aiding in architectural design and training autonomous agents to elevated user experiences in gaming and simulations. By enabling virtual humans with the ability to autonomously navigate and interact in crowded scenes, user systems can become more engaging and functional.

In theoretical terms, this work suggests promising pathways for integrating RL with generative models, presenting avenues for exploring more complex multi-agent interactions or inter-temporal object manipulations—each step closer to mimicking real-world intricacies.

Furthermore, while tackling real-time interaction synthesis, future explorations may delve into leveraging physics engines in conjunction with RL frameworks, potentially eliminating object penetration issues and enhancing system robustness.

In summation, "Synthesizing Diverse Human Motions in 3D Indoor Scenes" underpins essential advancements in the landscape of virtual human modeling, paving the way for dynamic, intelligent, and context-aware simulations in complex environments. The prospects of integrating such methodologies into broader systems signal a meaningful evolution toward realizing credible, immersive virtual worlds.