Abstract
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 of highlight and shadow detail precludes mapping accurate luminance to HDR displays and limits meaningful re-exposure in post-production workflows. Although techniques have been proposed to convert LDR images to HDR through dynamic range expansion, they struggle to restore realistic detail in the over- and underexposed regions. To address this, we present DiffHDR, a framework that formulates LDR-to-HDR conversion as a generative radiance inpainting task within the latent space of a video diffusion model. By operating in Log-Gamma color space, DiffHDR leverages spatio-temporal generative priors from a pretrained video diffusion model to synthesize plausible HDR radiance in over- and underexposed regions while recovering the continuous scene radiance of the quantized pixels. Our framework further enables controllable LDR-to-HDR video conversion guided by text prompts or reference images. To address the scarcity of paired HDR video data, we develop a pipeline that synthesizes high-quality HDR video training data from static HDRI maps. Extensive experiments demonstrate that DiffHDR significantly outperforms state-of-the-art approaches in radiance fidelity and temporal stability, producing realistic HDR videos with considerable latitude for re-exposure.
Abstract (translated)
大多数数字视频以8位低动态范围（LDR）格式存储，由于饱和和量化，原始高动态范围（HDR）场景的辐射值大量丢失。这种高光和阴影细节的缺失导致无法将精确的亮度映射到HDR显示器，并限制了后期制作流程中有意义的重新曝光。尽管已有技术通过动态范围扩展将LDR图像转换为HDR，但它们难以恢复过曝和欠曝区域中的真实细节。为此，我们提出了DiffHDR框架，该框架将LDR转HDR转换 formulate 为视频扩散模型潜在空间中的生成式辐射值修复任务。通过在Log-Gamma色彩空间中操作，DiffHDR利用预训练视频扩散模型中的时空生成先验，在过曝和欠曝区域合成合理的HDR辐射值，同时恢复量化像素的连续场景辐射值。我们的框架还支持通过文本提示或参考图像进行可控的LDR转HDR视频转换。为应对成对HDR视频数据的稀缺，我们开发了从静态HDRI贴图合成高质量HDR视频训练数据的流程。大量实验表明，DiffHDR在辐射值保真度和时间稳定性方面显著优于最先进方法，能够生成逼真的HDR视频，并具备大幅度的重新曝光空间。
URL
https://arxiv.org/abs/2604.06161
