3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework
Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant advances, current textured mesh scene reconstruction methods are far from artist ready, suffering from incorrect object decomposition, inaccurate spatial relationships, and missing backgrounds. We present 3D-RE-GEN, a compositional framework that reconstructs a single image into textured 3D objects and a background. We show that combining state of the art models from specific domains achieves state of the art scene reconstruction performance, addressing artists' requirements.
  Our reconstruction pipeline integrates models for asset detection, reconstruction, and placement, pushing certain models beyond their originally intended domains. Obtaining occluded objects is treated as an image editing task with generative models to infer and reconstruct with scene level reasoning under consistent lighting and geometry. Unlike current methods, 3D-RE-GEN generates a comprehensive background that spatially constrains objects during optimization and provides a foundation for realistic lighting and simulation tasks in visual effects and games. To obtain physically realistic layouts, we employ a novel 4-DoF differentiable optimization that aligns reconstructed objects with the estimated ground plane. 3D-RE-GEN~achieves state of the art performance in single image 3D scene reconstruction, producing coherent, modifiable scenes through compositional generation guided by precise camera recovery and spatial optimization.
6949054634f46eaf46cbb3f6
Tobias Sautter
6949054634f46eaf46cbb3f7
Jan-Niklas Dihlmann
claimed_verified
2025-12-22T10:58:21.425Z
631b6dbcbf1351ed2bd05be1
https://cdn-avatars.huggingface.co/v1/production/uploads/631b6dbcbf1351ed2bd05be1/_kzcDD4qCifsb-651_LmY.png
Jan-Niklas Dihlmann
JDihlmann
user
JDihlmann
6949054634f46eaf46cbb3f8
Hendrik P. A. Lensch
3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework
Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant advances, current textured mesh scene reconstruction methods are far from artist ready, suffering from incorrect object decomposition, inaccurate spatial relationships, and missing backgrounds. We present 3D-RE-GEN, a compositional framework that reconstructs a single image into textured 3D objects and a background. We show that combining state of the art models from specific domains achieves state of the art scene reconstruction performance, addressing artists' requirements.
  Our reconstruction pipeline integrates models for asset detection, reconstruction, and placement, pushing certain models beyond their originally intended domains. Obtaining occluded objects is treated as an image editing task with generative models to infer and reconstruct with scene level reasoning under consistent lighting and geometry. Unlike current methods, 3D-RE-GEN generates a comprehensive background that spatially constrains objects during optimization and provides a foundation for realistic lighting and simulation tasks in visual effects and games. To obtain physically realistic layouts, we employ a novel 4-DoF differentiable optimization that aligns reconstructed objects with the estimated ground plane. 3D-RE-GEN~achieves state of the art performance in single image 3D scene reconstruction, producing coherent, modifiable scenes through compositional generation guided by precise camera recovery and spatial optimization.
3D-RE-GEN presents a compositional framework for reconstructing single images into textured 3D scenes with improved object decomposition, spatial relationships, and background generation through integrated models and differentiable optimization.
3D scene generation textured mesh scenes image editing task generative models scene level reasoning camera recovery spatial optimization 4-DoF differentiable optimization ground plane alignment