RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting
Quick summary
arXiv:2609.07414v1 Announce Type: cross Abstract: Image relighting is traditionally tackled via complex inverse rendering pipelines, which suffer from ill-posed optimization, or single-image generative models that ignore crucial multi-view cues necessary for understanding 3D geometry and material interactions. To address these limitations, we introduce a feed-forward generative Transformer for direct single- and multi-view image relighting that entirely bypasses explicit intrinsic property estimation. Adapted from a video foundation model, our architecture features a latent illumination module
Key takeaways
- arXiv:2609.07414v1 Announce Type: cross Abstract: Image relighting is traditionally tackled via complex inverse rendering pipelines, which suffer from ill-posed optimization, or single-image generative models that ignore crucial multi-view cues necessary for understanding 3D geometry and material interactions.
- To address these limitations, we introduce a feed-forward generative Transformer for direct single- and multi-view image relighting that entirely bypasses explicit intrinsic property estimation.
- Adapted from a video foundation model, our architecture features a latent illumination module
Why it matters
“RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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