We present FIRE3D, a unified framework that transforms a single RGB image or casual video into interactable 3D scene assets for games and interactive applications in under a minute for up to twelve textured instances including preprocessing. At the core of FIRE3D is a feed-forward inference pipeline 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 detected objects. By modeling the scene as a collection of discrete entities, FIRE3D produces amodal object assets that can be independently edited and used in interactive applications. Our framework requires no test-time optimization, runs substantially faster than the compared reconstruction systems, and provides object-level completeness beyond existing feed-forward 3D approaches. We demonstrate leading detection accuracy, strong geometry reconstruction, and competitive rendering quality on the evaluated benchmarks, with substantial runtime gains.
These readouts suggest that program-constructed scenes from current coding agents are a promising but not yet sufficiently precise representation of natural images, suggesting that program-constructed scenes from current coding agents are a promising but not yet sufficiently precise representation of natural images.
Xi-Rui Li, Peng Shi, Ming-Wen Dong et al.· 0 citations
We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene. Existing methods often generate objects independently or couple them implicitly, providing limited guidance for ensuring fine-grained spatial compatibili...
Jaeyeon Kim, Jinhyuk Jang, Jongmin Lee et al.· 0 citations
Reconstructing complete, scene-aligned 3D objects from casual images requires integrating sparse, uncertain observations and inferring surfaces hidden by occlusions. We present GATOR, a generative and agentic framework that recovers textured object assets and their scene-relative pose from one or more images. Our local...
Qi-Rui Wu, Stanley T. Birchfield, Hesam Rabeti et al.· 0 citations
3DCoder, a framework that converts a single image into a structured and executable 3D representation suitable for editing and physics-based simulation, and a dataset that jointly annotates part geometry, kinematic hierarchies, articulation parameters, materials, and non-tree relations across articulated objects, proced...
We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view generation for unobserved regions and geometric consistency, providing both geometrically aligned novel views and sharp depth estimates. Our approach builds on 3D Gaussian spl...
Giuseppe Stracquadanio, Kevin Raj, Julia Grabinski et al.· 0 citations
AnyGS2Mesh is presented, the first feed-forward framework for directly reconstructing 3D meshes from 3D Gaussian Splatting representations with support for arbitrary input image resolutions, and demonstrates the potential of combining Gaussian representations and feed-forward Transformer architectures for scalable 3D g...
Yuxuan Song, Fan Gao, Yi-Bo Zhao et al.· 0 citations
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