[Paper] Under One Sun: Multi-Object Generative Perception of Materials and Illumination
Source: arXiv - 2603.19226v1
Overview
We introduce Multi-Object Generative Perception (MultiGP), a generative inverse rendering method for stochastic sampling of all radiometric constituents — reflectance, texture, and illumination — underlying object appearance from a single image. Our key idea to solve this inherently ambiguous radiometric disentanglement is to leverage the fact that while their texture and reflectance may differ, objects in the same scene are all lit by the same illumination. MultiGP exploits this consensus to produce samples of reflectance, texture, and illumination from a single image of known shapes based on four key technical contributions: a cascaded end-to-end architecture that combines image-space and angular-space disentanglement; Coordinated Guidance for diffusion convergence to a single consistent illumination estimate; Axial Attention applied to facilitate “cross-talk” between objects of different reflectance; and a Texture Extraction ControlNet to preserve high-frequency texture details while ensuring decoupling from estimated lighting. Experimental results demonstrate that MultiGP effectively leverages the complementary spatial and frequency characteristics of multiple object appearances to recover individual texture and reflectance as well as the common illumination.
Key Contributions
This paper presents research in the following areas:
- cs.CV
Methodology
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Practical Implications
This research contributes to the advancement of cs.CV.
Authors
- Nobuo Yoshii
- Xinran Nicole Han
- Ryo Kawahara
- Todd Zickler
- Ko Nishino
Paper Information
- arXiv ID: 2603.19226v1
- Categories: cs.CV
- Published: March 19, 2026
- PDF: Download PDF