A multi-exposure video model predicts bracketed linear SDR sequences from single nonlinear SDR input, which a merging model combines into HDR video preserving shadow and highlight detail.
arXiv preprint arXiv:2602.04814 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 4years
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UNVERDICTED 4representative citing papers
Single-shot HDR is achieved by conditioning a video diffusion model on an LDR input to generate an exposure bracket and fusing the bracket with per-pixel weights from a lightweight UNet.
HDR video generation is achieved by logarithmically encoding HDR imagery to align with pretrained generative model latents, enabling minimal fine-tuning and degradation-based inference of missing content.
DiffHDR converts LDR videos to HDR by formulating the task as generative radiance inpainting in a video diffusion model's latent space, using Log-Gamma encoding and synthesized training data to achieve better fidelity and stability than prior methods.
citing papers explorer
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Generating HDR Video from SDR Video
A multi-exposure video model predicts bracketed linear SDR sequences from single nonlinear SDR input, which a merging model combines into HDR video preserving shadow and highlight detail.
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Single-Shot HDR Recovery via a Video Diffusion Prior
Single-shot HDR is achieved by conditioning a video diffusion model on an LDR input to generate an exposure bracket and fusing the bracket with per-pixel weights from a lightweight UNet.
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HDR Video Generation via Latent Alignment with Logarithmic Encoding
HDR video generation is achieved by logarithmically encoding HDR imagery to align with pretrained generative model latents, enabling minimal fine-tuning and degradation-based inference of missing content.
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DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models
DiffHDR converts LDR videos to HDR by formulating the task as generative radiance inpainting in a video diffusion model's latent space, using Log-Gamma encoding and synthesized training data to achieve better fidelity and stability than prior methods.