Challenges of scaling 3D environments in reflected AI
Creating realistic and accurate 3D environments is essential to train and evaluate the embodied AI. However, the current approach still relies on manually designed 3D graphics that are expensive and lack realism, thus limiting scalability and generalization. Unlike Internet-scale data used in models such as GPT and CLIP, the AI data reflected is expensive, context-specific and difficult to reuse. Achieving universal intelligence in a physical environment requires realistic simulation, reinforcement learning and different 3D assets. Although recent diffusion models and 3D generation techniques show promise, many still lack key features such as body accuracy, watertight geometry and correct scales that prevent them from being used in robot training environments.
Limitations of existing 3D generation technology
3D object generation usually follows three main methods: feedforward generation of fast results, high-quality methods based on optimization, and viewing and reconstruction from multiple images. Although recent techniques improve realism by separating geometric shapes and texture creation, many models still prioritize visual appearance over real-world physics. This makes them unsuitable for simulations requiring accurate scaling and watertight geometry. For 3D scenes, panoramic technology has enabled full view rendering, but they still lack interactivity. Although some tools attempt to enhance the simulated environment through the generated assets, quality and diversity are still limited, but lack due to the complexity of embodying the need for intelligent research.
Introduced: Open source, modular and simulation ready
Enbodiedgen is an open source framework developed by researchers from Horizon Robotics, University of Hong Kong, Shanghai Qi Zhi Institute and Tsinghua University. It aims to generate realistic, scalable 3D assets that embody AI tasks. The platform is output in physically accurate, watertight 3D object in Uld F format and is equipped with metadata for compatibility. It has six modular components including image to 3D, text to 3D, layout generation and object rearrangement, enabling controllable and efficient scene creation. By bridging the gap between traditional 3D graphics and robot-prepared assets, it embodies the scalability and cost-effective development of the interactive environment for reflective intelligent research.
Main features: Multi-mode generation of 3D rich content
Enbodiedgen is a multi-function toolkit designed to generate realistic and interactive 3D environments that embody AI tasks. It combines multiple generation modules: converting images or text into detailed 3D objects, creating articulated projects with movable parts, and generating multiple textures for improved visual quality. It also supports complete scenario building by arranging these assets with respect to the physical characteristics and scale of the real world. This output is directly compatible with the simulation platform, making it easier and more affordable to build a lifelike virtual world. The system helps researchers effectively simulate real-world situations without relying on expensive manual modeling.
Simulation integration and real-world body accuracy
Enbodiedgen is a powerful and easy-to-use platform that enables a wide range of high-quality 3D assets for smart research. It has several key modules that allow users to create assets from images or text, generate clear and textured objects, and build realistic scenes. These assets are watertight, realistic, and physically accurate, making them ideal for simulation-based training and evaluation based on robotics. The platform supports integration with popular simulation environments including OpenAI gym, Mujoco, Isaac Lab and Sapien, enabling researchers to effectively simulate tasks such as navigation, object manipulation and obstacle avoidance in a low-cost way.
Robosplatter: Hi-Fi 3DGS Rendering for Simulation
One notable feature is Robosplatter, which renders advanced 3D Gaussian shedding (3DG) into a physical simulation. Unlike traditional graphics pipelines, Robosplatter enhances visual fidelity while reducing computational overhead. With modules such as texture generation and real SIM conversion, users can edit the appearance of 3D assets or recreate real-world scenarios with higher realism. Overall, the roots embodied simplify the creation of a scalable interactive 3D world, thus bridging the gap between real-world robotics and digital simulations. It can be used publicly as a user-friendly toolkit to support wider adoption and continue to innovate in reflecting AI research.
Why is this research important?
This study addresses the core bottleneck embodying AI: the lack of scalable, realistic and physically compatible 3D environments for training and evaluation. Although Internet-scale data drives advances in visual and language models, specific intelligence requires simulation-ready assets at accurate scale, geometry and interactivity – often lacking quality in traditional 3D generations. Enbodiedgen fills this gap by providing an open source, modular platform that produces high-quality, controllable 3D objects and scenarios compatible with the major robot simulators. Its ability to convert text and images into physically sound 3D environments at scale makes it the fundamental tool for advancing the embodied AI research, digital twins and real learning.
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Sana Hassan, a consulting intern at Marktechpost and a dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. He is very interested in solving practical problems, and he brings a new perspective to the intersection of AI and real-life solutions.
