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The University of Texas at Austin
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Aligning luminance histograms can transform diffusion models into powerful HDR generators without any retraining, achieving remarkable fidelity and detail.
Consistent spherical parameterizations can be learned across diverse shapes, drastically reducing geometric distortion and enhancing model robustness.
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.
