Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910144549355520 |
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| author | Sun, Shanlin Wang, Yifan Zhang, Hanwen Xiong, Yifeng Ren, Qin Fang, Ruogu Xie, Xiaohui You, Chenyu |
| author_facet | Sun, Shanlin Wang, Yifan Zhang, Hanwen Xiong, Yifeng Ren, Qin Fang, Ruogu Xie, Xiaohui You, Chenyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering Sun, Shanlin Wang, Yifan Zhang, Hanwen Xiong, Yifeng Ren, Qin Fang, Ruogu Xie, Xiaohui You, Chenyu Computer Vision and Pattern Recognition 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. |
| title | Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.14461 |