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LatentUMM introduces dual latent alignment to improve cross-modal consistency in unified multimodal models by aligning transformations and stabilizing latent dynamics.
This paper presents a method for HDR video generation by leveraging pretrained generative models through logarithmic encoding alignment and camera-mimicking degradation training, enabling effective HDR synthesis without architectural redesign. The approach demonstrates that HDR generation can be achieved simply by adapting existing models to a representation naturally aligned with their learned priors.