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Aligning luminance histograms can transform diffusion models into powerful HDR generators without any retraining, achieving remarkable fidelity and detail.
A new large-scale dataset of human-annotated video crops enables training models that adapt videos to different aspect ratios while preserving visual quality and meaning.
Control video super-resolution with a few keyframes: SparkVSR lets you guide the process and fix artifacts, unlike black-box VSR models.
The YT-NTU-AVQ dataset, 10x larger than previous AVQA datasets, unlocks new possibilities for training and evaluating multimodal perception models by offering unprecedented scale and diversity.
