PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI (2404.09465v2)

Abstract: With recent developments in Embodied Artificial Intelligence (EAI) research, there has been a growing demand for high-quality, large-scale interactive scene generation. While prior methods in scene synthesis have prioritized the naturalness and realism of the generated scenes, the physical plausibility and interactivity of scenes have been largely left unexplored. To address this disparity, we introduce PhyScene, a novel method dedicated to generating interactive 3D scenes characterized by realistic layouts, articulated objects, and rich physical interactivity tailored for embodied agents. Based on a conditional diffusion model for capturing scene layouts, we devise novel physics- and interactivity-based guidance mechanisms that integrate constraints from object collision, room layout, and object reachability. Through extensive experiments, we demonstrate that PhyScene effectively leverages these guidance functions for physically interactable scene synthesis, outperforming existing state-of-the-art scene synthesis methods by a large margin. Our findings suggest that the scenes generated by PhyScene hold considerable potential for facilitating diverse skill acquisition among agents within interactive environments, thereby catalyzing further advancements in embodied AI research. Project website: http://physcene.github.io.

Overview of PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI

The paper "PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI" presents a novel approach for generating interactive 3D environments tailored specifically for embodied artificial intelligence (EAI) agents. This research addresses a significant gap in existing scene synthesis methodologies by emphasizing physical plausibility and interactivity, which are crucial for developing realistic and functional training environments.

Key Contributions

1. Introduction of PhyScene: PhyScene leverages a guided diffusion model to generate 3D scenes that not only appear realistic but also adhere to physical laws and allow for meaningful interaction. The proposed framework integrates constraints on object collision, room layout, and object reachability into the scene generation process, ensuring that the resulting scenes are both plausible and interactable.
2. Guidance Functions:

The methodology uses three novel guidance functions: - Collision Avoidance: Minimizes collisions between objects by applying constraints based on 3D object bounding boxes. - Room-Layout Guidance: Ensures objects are placed within floor plan boundaries, reducing inter-room conflicts. - Reachability Guidance: Enhances agent interactiveness by ensuring objects are reachable and maintaining navigable space.

1. Experimental Results: Extensive experiments demonstrate that PhyScene outperforms existing state-of-the-art methods by a considerable margin. PhyScene achieves better results on traditional scene synthesis metrics and shows significant improvements in physical plausibility and interactivity.

Technical Details

The PhyScene approach is based on a conditional diffusion model. The core idea is to learn the distribution of 3D scene layouts and apply physic-based constraints during the generation process to ensure physical interactivity.

Object Representation

Objects in scenes are represented by semantic labels, sizes, orientations, locations, and 3D geometric features. This allows the model to effectively retrieve and integrate articulated objects from various datasets, such as 3D-FUTURE and GAPartNet.

Conditional Diffusion for Layout Modeling

The forward diffusion process involves gradually adding Gaussian noise to a data sample, while the reverse process attempts to reconstruct the original data. Conditions such as the floor plan are embedded within the diffusion model to guide the generation towards realistic and physically plausible scenes.

Guidance Functions

Guidance functions are integral to the denoising process and provide additional constraints to ensure the generated scenes meet the desired physical criteria. Each function handles a specific aspect of physical plausibility:

• Collision Avoidance: Calculated using the Intersection over Union (IoU) of 3D bounding boxes.
• Room-Layout Guidance: Ensures objects stay within designated room boundaries.
• Reachability Guidance: Uses shortest path algorithms to maintain navigable space within the scene.

Implications and Future Work

The implications of PhyScene are multifaceted, impacting both practical applications and theoretical advancements in AI. Practically, PhyScene enables the creation of more realistic and interactable training environments for EAI agents, which can lead to better performance in real-world tasks. Theoretically, this research bridges a crucial gap in scene synthesis by integrating physical constraints, paving the way for more advanced models that consider even more intricate interactions and object properties.

Conclusion

PhyScene presents a robust framework for generating physically plausible and interactable 3D scenes. By incorporating guided diffusion models with novel physical constraints, it sets a new benchmark in scene synthesis for embodied AI. Future research could explore extending PhyScene to include more complex interactions, micro-scale object manipulations, and broader environment types, further enhancing the versatility and applicability of EAI systems.

This paper is a significant step forward in the field of scene synthesis, providing a comprehensive solution for creating interactive 3D environments essential for advancing embodied AI research.