EmbodiedGen: Revolutionizing Scalable 3D World Generation for Embodied AI

Challenges in Scaling 3D Environments for Embodied AI

Creating realistic and accurately scaled 3D environments is crucial for training and evaluating embodied AI systems. Current approaches largely depend on manually designed 3D graphics, which are expensive and lack sufficient realism, limiting scalability and generalization. Unlike large-scale internet data used for models like GPT and CLIP, embodied AI data is costly, context-specific, and difficult to reuse. Achieving general-purpose intelligence in physical environments requires realistic simulations, reinforcement learning, and a wide variety of 3D assets. Although recent diffusion models and 3D generation techniques have shown promise, many still fall short in essential features such as physical accuracy, watertight geometry, and correct scale, making them unsuitable for robotic training.

Limitations of Existing 3D Generation Methods

Three main approaches dominate 3D object generation: feedforward generation for speed, optimization-based methods for quality, and multi-view reconstruction. While advancements have improved visual realism by separating geometry and texture, many models prioritize appearance over physical fidelity. This compromises their suitability for simulations that need accurate scale and watertight geometry. Panoramic techniques enable full-view rendering but lack interactivity. Attempts to enhance simulation environments with generated assets have not reached the quality or diversity required by complex embodied AI research.

Introducing EmbodiedGen: An Open-Source, Modular Framework

EmbodiedGen is a collaborative open-source platform developed by researchers from Horizon Robotics, the Chinese University of Hong Kong, Shanghai Qi Zhi Institute, and Tsinghua University. It is crafted to generate realistic, scalable 3D assets specifically for embodied AI tasks. The framework produces physically accurate, watertight 3D objects in URDF format with metadata for simulation compatibility. EmbodiedGen consists of six modular components, including image-to-3D, text-to-3D, layout generation, and object rearrangement, enabling controllable and efficient scene composition. This bridges the gap between traditional 3D graphics and robotics-ready assets, facilitating scalable and cost-effective creation of interactive environments for embodied intelligence research.

Key Features: Multi-Modal Generation for Diverse 3D Content

EmbodiedGen supports multiple generation modes: converting images or text into detailed 3D objects, creating articulated items with movable parts, and generating diverse textures to enhance visual quality. It facilitates full scene construction by arranging assets with respect to real-world physical properties and scale. Outputs are simulation-ready and compatible with popular platforms, simplifying the development of lifelike virtual worlds. This toolkit enables researchers to simulate real-world scenarios efficiently without costly manual modeling.

Simulation Integration and Physical Realism

The platform generates watertight, photorealistic, and physically accurate assets ideal for simulation-based robotics training and evaluation. It integrates seamlessly with simulation environments such as OpenAI Gym, MuJoCo, Isaac Lab, and SAPIEN, supporting tasks like navigation, object manipulation, and obstacle avoidance at a low cost.

RoboSplatter: Enhancing Rendering Fidelity for Simulation

RoboSplatter is a standout feature that introduces advanced 3D Gaussian Splatting (3DGS) rendering into physical simulations. Unlike traditional graphics pipelines, it improves visual fidelity while reducing computational demands. Modules like Texture Generation and Real-to-Sim conversion allow editing of 3D asset appearances and recreation of real-world scenes with high realism. EmbodiedGen thus simplifies scalable, interactive 3D world creation, bridging real-world robotics and digital simulation.

Significance of This Research

EmbodiedGen addresses a critical bottleneck in embodied AI: the lack of scalable, realistic, and physics-compatible 3D environments for training and evaluation. While internet-scale data propelled vision and language model progress, embodied intelligence requires simulation-ready assets with precise scale, geometry, and interactivity. This open-source, modular platform produces high-quality, controllable 3D objects and scenes compatible with major robotics simulators. Its capacity to convert text and images into physically plausible 3D environments at scale makes it foundational for advancing embodied AI, digital twins, and real-to-sim learning.

Additional Resources

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