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.
2304.13625 , archivePrefix=
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.CV 9years
2026 9verdicts
UNVERDICTED 9roles
background 2polarities
background 2representative 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.
LatentHDR generates structurally consistent panoramic HDR images by producing one scene latent with a diffusion backbone then deterministically mapping it to multiple exposure latents via a lightweight conditional head.
ExpoCM enables fast one-step single-image HDR reconstruction via exposure-dependent perturbations and region-conditioned consistency trajectories derived from a probability flow ODE.
An exposure-decoupled modulo formulation and iteration-free diffusion-prior unwrapping enable 1000 FPS full-color HDR imaging on spike cameras while cutting bandwidth from 20 Gbps to 6 Gbps.
LumaFlux is a physically and perceptually guided diffusion transformer for SDR-to-HDR conversion that introduces PGA, PCM, and HDR Residual Coupler modules plus a new training corpus and benchmark, outperforming prior ITM methods.
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.
FDIM is a new hybrid feature-distance video quality metric trained on over 16k sequences that shows strong generalization and correlation with human judgments across ten unseen SDR/HDR datasets and diverse codecs.
citing papers explorer
-
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.
-
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.
-
LatentHDR: Decoupling Exposure from Diffusion via Conditional Latent-to-Latent Mapping for Text/Image-to-Panoramic HDR
LatentHDR generates structurally consistent panoramic HDR images by producing one scene latent with a diffusion backbone then deterministically mapping it to multiple exposure latents via a lightweight conditional head.
-
ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction
ExpoCM enables fast one-step single-image HDR reconstruction via exposure-dependent perturbations and region-conditioned consistency trajectories derived from a probability flow ODE.
-
High-Speed Full-Color HDR Imaging via Unwrapping Modulo-Encoded Spike Streams
An exposure-decoupled modulo formulation and iteration-free diffusion-prior unwrapping enable 1000 FPS full-color HDR imaging on spike cameras while cutting bandwidth from 20 Gbps to 6 Gbps.
-
LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers
LumaFlux is a physically and perceptually guided diffusion transformer for SDR-to-HDR conversion that introduces PGA, PCM, and HDR Residual Coupler modules plus a new training corpus and benchmark, outperforming prior ITM methods.
-
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.
-
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.
-
FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs
FDIM is a new hybrid feature-distance video quality metric trained on over 16k sequences that shows strong generalization and correlation with human judgments across ten unseen SDR/HDR datasets and diverse codecs.