RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting

• URL: http://arxiv.org/abs/2609.07414v1
• Date: Mon, 07 Sep 2026 12:25:58 GMT
• Title: RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting
• Abstract summary: We introduce a feed-forward generative Transformer for direct single- and multi-view image relighting.<n>Our architecture features a latent illumination module that injects target environment maps into spatial features via cross-attention.
• Score: 9.754843139832921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 that dynamically injects target environment maps into spatial features via cross-attention. Furthermore, we employ permutation-invariant positional encodings to symmetrically process unordered multi-view inputs without sequential bias. To train this robust data-driven model, we construct the massive Laval Objaverse Dataset (LOD), comprising 90K objects and 39K unique illuminations. Extensive experiments demonstrate state-of-the-art visual quality, photorealistic relighting quality, and strong zero-shot generalization across single-view, multi-view, and novel-view relighting tasks.

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