用单步扩散模型实现前后向渲染一致性，速度更快。
Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering
- 双单步扩散模型互为增强，实现前后向渲染协同。
- 在多类场景中达到顶尖性能，推理速度显著提升。
- 无需训练即可用于视频分解，减少时序不一致。
尽管多步扩散模型已推动前向与逆向渲染的发展，现有方法通常独立处理二者，导致循环不一致且推理缓慢。本文提出Ouroboros框架，由两个单步扩散模型组成，通过相互强化实现前向与逆向渲染的统一。该方法将固有分解扩展至室内外场景，并引入循环一致性机制，确保前后输出的一致性。实验表明，Ouroboros在多样化场景中表现优于现有方法，推理速度显著提升。此外，该模型可零样本迁移至视频分解任务，在保持帧级高质量逆向渲染的同时，有效降低视频序列中的时序不一致问题。
原文摘要 · Abstract (English)
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.
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