HDR Video Generation via Latent Alignment with Logarithmic Encoding

The project addresses a critical bottleneck in generative video: the discrepancy between the limited dynamic range of training data (SDR) and the requirements of professional cinematography (HDR). By using log-encoding to compress the high dynamic range into a distribution that existing diffusion models can process, it allows for 'zero-shot' or 'low-shot' adaptation of massive pre-trained models. However, its defensibility is low (Score 3) because it is primarily a methodological breakthrough rather than a product with structural moats. While the 9 forks in 4 days indicate high interest from the research community, the lack of stars suggests it hasn't yet transitioned to a tool for practitioners. Frontier labs (OpenAI, Runway, Luma) have a high risk of displacing this; as they scale their training datasets, they are likely to incorporate native HDR support or develop proprietary log-space latent representations that make this specific alignment technique obsolete. The platform domination risk is high because professional HDR workflows are controlled by a few giants (Adobe, Blackmagic, Google) who can easily integrate these mathematical transformations into their pipelines. This project serves as a brilliant bridge technology, but its long-term survival as a standalone entity is unlikely once foundation models support wider bit-depths natively.