Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion

TL;DR

Uni-Renderer introduces a unified diffusion-based framework that jointly models rendering and inverse rendering, improving consistency and reducing ambiguity in a data-driven manner.

Contribution

It proposes a novel dual-stream diffusion approach that unifies rendering and inverse rendering within a single model, enabling mutual facilitation and cycle consistency.

Findings

Effective decomposition of intrinsic properties from images

Strong capability to recognize changes during rendering

Unified framework reduces ambiguity and improves consistency

Abstract

Rendering and inverse rendering are pivotal tasks in both computer vision and graphics. The rendering equation is the core of the two tasks, as an ideal conditional distribution transfer function from intrinsic properties to RGB images. Despite achieving promising results of existing rendering methods, they merely approximate the ideal estimation for a specific scene and come with a high computational cost. Additionally, the inverse conditional distribution transfer is intractable due to the inherent ambiguity. To address these challenges, we propose a data-driven method that jointly models rendering and inverse rendering as two conditional generation tasks within a single diffusion framework. Inspired by UniDiffuser, we utilize two distinct time schedules to model both tasks, and with a tailored dual streaming module, we achieve cross-conditioning of two pre-trained diffusion models. This unified approach, named Uni-Renderer, allows the two processes to facilitate each other through a cycle-consistent constrain, mitigating ambiguity by enforcing consistency between intrinsic properties and rendered images. Combined with a meticulously prepared dataset, our method effectively decomposition of intrinsic properties and demonstrates a strong capability to recognize changes during rendering. We will open-source our training and inference code to the public, fostering further research and development in this area.