Native-HDR Relighting with Pixel-Space Flow Models

We present the first native high-dynamic-range (HDR) pixel-space model for single-image relighting. Fine-tuned only on paired synthetic single-object images, it generalizes far beyond this fine-tuning distribution and outperforms previous methods on even complex real-world scenes. This is achieved by three design choices. First, whereas previous methods modify the pretrained model's conditioning pathways to fit the relighting conditions, we instead conform the conditions to the model: express the conditions in a form the model fully accepts, which preserves the model's pretrained knowledge. Second, whereas most previous methods produce only low-dynamic-range (LDR) outputs through LDR-trained variational autoencoders (VAEs), our model predicts native HDR directly in pixel space. HDR more closely represents the physical nature of light transport, and predicting in pixel space avoids the lossy reconstruction and representational constraints of such VAEs, preserving fine source details and simplifying HDR fine-tuning. Third, native HDR output lets us formulate a linearity constraint grounded in the additivity of light transport, which maintains consistency better than previous formulations that operate in the latent space of LDR-trained VAEs.