Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering
This work addresses rendering challenges for computer vision and graphics applications, offering an incremental improvement by integrating cycle consistency into a faster diffusion-based approach.
The paper tackles the problem of cycle inconsistency and slow inference in forward and inverse rendering by introducing Ouroboros, a framework with two single-step diffusion models that achieve state-of-the-art performance and substantially faster inference speed across diverse scenes.
While multi-step diffusion models have advanced both forward and inverse rendering, existing approaches often treat these problems independently, leading to cycle inconsistency and slow inference speed. In this work, we present Ouroboros, a framework composed of two single-step diffusion models that handle forward and inverse rendering with mutual reinforcement. Our approach extends intrinsic decomposition to both indoor and outdoor scenes and introduces a cycle consistency mechanism that ensures coherence between forward and inverse rendering outputs. Experimental results demonstrate state-of-the-art performance across diverse scenes while achieving substantially faster inference speed compared to other diffusion-based methods. We also demonstrate that Ouroboros can transfer to video decomposition in a training-free manner, reducing temporal inconsistency in video sequences while maintaining high-quality per-frame inverse rendering.