arXiv Computer Vision
Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction
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Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene
arXiv:2609.23796v1 Announce Type: new Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challe...
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SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images
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arXiv Computer Vision
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GenIA is a framework that aligns generative 3D foundation models with test‑time observations, improving pose estimation and reconstruction from monocular, multi‑view, and dynamic inputs. It derives translation and scale from geometry, retains learned rotation priors, and aligns appearance using visibility‑biased attention, cross‑observation fusion, and differentiable rendering guidance during denoising. An optional post‑denoising refinement further adapts appearance latents and object placement, and the method also supports externally supplied geometry for dynamic objects, achieving better results than recent optimization‑based and image‑to‑3D methods on synthetic and real benchmarks.