Fire3D creates interactive 3D scenes from images fast
FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute
Computer Vision and Pattern RecognitionRobotics
Summary
Turning pictures or casual videos into 3D scenes that can be used in games and apps usually takes a long time and a lot of work. The researchers developed Fire3D, a system that quickly makes 3D models of scenes and separates objects for interaction, all in under a minute. It estimates object positions, sizes, shapes, and textures directly from images without slow adjustments afterward. This method is much faster than previous ones and produces detailed, ready-to-use 3D scenes.
What this means in practice
- •For game developers: Generate ready-to-use 3D objects and environments from photos in under a minute for quicker game content creation.$Commercial implications: Enables production of interactive 3D assets quickly from casual captures, streamlining game development pipelines.
- •For augmented reality developers: Create compositional and interactive 3D scene models from regular video to enhance real-world AR experiences.
Authors
Hongchi Xia, Tianhang Cheng, Wei-Chiu Ma, Shenlong Wang
Abstract
We present FIRE3D, a unified framework that takes a single RGB image or casual RGB video and transforms it into simulation-ready 3D scene assets for games and interactive applications in under a minute. At the core of FIRE3D is a feed-forward, end-to-end network that predicts a compositional scene representation from posed RGB-D observations estimated from the RGB capture, including the 6-DoF pose, bounding box, mesh, and texture for every object. By modeling the scene as a collection of discrete entities, FIRE3D produces amodally complete and simulation-ready environments where objects are physically decoupled and ready for interaction. Our framework requires no test-time optimization, runs orders of magnitude faster than prior interaction-ready methods, and provides object-level completeness beyond existing feed-forward 3D approaches. We demonstrate competitive or state-of-the-art results across pose accuracy, geometry completeness, and texture quality across various datasets while being orders of magnitudes faster. Project page: https://xiahongchi.github.io/Fire3D/