EmbodiedGen: Towards a Generative 3D World Engine for Embodied
  Intelligence
Authors: name: Wang Xinjie, name: Liu Liu, name: Cao Yu, name: Wu Ruiqi, name: Qin Wenkang, name: Wang Dehui, name: Sui Wei, name: Su Zhizhong

Constructing a physically realistic and accurately scaled simulated 3D world
is crucial for the training and evaluation of embodied intelligence tasks. The
diversity, realism, low cost accessibility and affordability of 3D data assets
are critical for achieving generalization and scalability in embodied AI.
However, most current embodied intelligence tasks still rely heavily on
traditional 3D computer graphics assets manually created and annotated, which
suffer from high production costs and limited realism. These limitations
significantly hinder the scalability of data driven approaches. We present
EmbodiedGen, a foundational platform for interactive 3D world generation. It
enables the scalable generation of high-quality, controllable and
photorealistic 3D assets with accurate physical properties and real-world scale
in the Unified Robotics Description Format (URDF) at low cost. These assets can
be directly imported into various physics simulation engines for fine-grained
physical control, supporting downstream tasks in training and evaluation.
EmbodiedGen is an easy-to-use, full-featured toolkit composed of six key
modules: Image-to-3D, Text-to-3D, Texture Generation, Articulated Object
Generation, Scene Generation and Layout Generation. EmbodiedGen generates
diverse and interactive 3D worlds composed of generative 3D assets, leveraging
generative AI to address the challenges of generalization and evaluation to the
needs of embodied intelligence related research. Code is available at
https://horizonrobotics.github.io/robot_lab/embodied_gen/index.html.