DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models

Most digital videos are stored in 8-bit low dynamic range(LDR) formats, where much of the original high dynamic range (HDR)scene radiance is lost due to saturation and quantization. This loss ofhighlight and shadow detail precludes mapping accurate luminance toHDR displays and limits meaningful re-exposure in post-production work-flows. Although techniques have been proposed to convert LDR imagesto HDR through dynamic range expansion, they struggle to restore re-alistic detail in the over- and underexposed regions. To address this, wepresent DiffHDR, a framework that formulates LDR-to-HDR conversionas a generative radiance inpainting task within the latent space of a videodiffusion model. By operating in Log-Gamma color space, DiffHDR lever-ages spatio-temporal generative priors from a pretrained video diffusionmodel to synthesize plausible HDR radiance in over- and underexposedregions while recovering the continuous scene radiance of the quantizedpixels. Our framework further enables controllable LDR-to-HDR videoconversion guided by text prompts or reference images. To address thescarcity of paired HDR video data, we develop a pipeline that synthesizeshigh-quality HDR video training data from static HDRI maps. Extensiveexperiments demonstrate that DiffHDR significantly outperforms state-of-the-art approaches in radiance fidelity and temporal stability, produc-ing realistic HDR videos with considerable latitude for re-exposure.