FlowIID: Single-Step Intrinsic Image Decomposition via Latent Flow Matching
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| Format: | Preprint |
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2026
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| _version_ | 1866917209288212480 |
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| author | Singla, Mithlesh Kumari, Seema Raman, Shanmuganathan |
| author_facet | Singla, Mithlesh Kumari, Seema Raman, Shanmuganathan |
| contents | Intrinsic Image Decomposition (IID) separates an image into albedo and shading components. It is a core step in many real-world applications, such as relighting and material editing. Existing IID models achieve good results, but often use a large number of parameters. This makes them costly to combine with other models in real-world settings. To address this problem, we propose a flow matching-based solution. For this, we design a novel architecture, FlowIID, based on latent flow matching. FlowIID combines a VAE-guided latent space with a flow matching module, enabling a stable decomposition of albedo and shading. FlowIID is not only parameter-efficient, but also produces results in a single inference step. Despite its compact design, FlowIID delivers competitive and superior results compared to existing models across various benchmarks. This makes it well-suited for deployment in resource-constrained and real-time vision applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_12329 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FlowIID: Single-Step Intrinsic Image Decomposition via Latent Flow Matching Singla, Mithlesh Kumari, Seema Raman, Shanmuganathan Computer Vision and Pattern Recognition Intrinsic Image Decomposition (IID) separates an image into albedo and shading components. It is a core step in many real-world applications, such as relighting and material editing. Existing IID models achieve good results, but often use a large number of parameters. This makes them costly to combine with other models in real-world settings. To address this problem, we propose a flow matching-based solution. For this, we design a novel architecture, FlowIID, based on latent flow matching. FlowIID combines a VAE-guided latent space with a flow matching module, enabling a stable decomposition of albedo and shading. FlowIID is not only parameter-efficient, but also produces results in a single inference step. Despite its compact design, FlowIID delivers competitive and superior results compared to existing models across various benchmarks. This makes it well-suited for deployment in resource-constrained and real-time vision applications. |
| title | FlowIID: Single-Step Intrinsic Image Decomposition via Latent Flow Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.12329 |