Generating HDR Video from SDR Video
- URL: http://arxiv.org/abs/2605.14703v1
- Date: Thu, 14 May 2026 11:21:10 GMT
- Title: Generating HDR Video from SDR Video
- Abstract summary: We propose a framework for HDR video synthesis from casual SDR footage by leveraging large-scale generative video models.<n>Our approach enables robust HDR conversion for in-the-wild examples from casual consumer videos and even iconic films.
- Score: 23.429962854122966
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persists without a convincing solution. We propose a framework for HDR video synthesis from casual SDR footage by leveraging large-scale generative video models. We introduce a Multi-Exposure Video Model (MEVM) that can predict exposure-bracketed linear SDR video sequences from a single nonlinear SDR video input. We further propose a learnable Video Merging Model (VMM) that merges the predicted exposure-bracketed video into a high-quality HDR sequence while preserving detail in both shadows and highlights. Extensive experiments, quantitative and qualitative evaluation, and a user study demonstrate that our approach enables robust HDR conversion for in-the-wild examples from casual consumer videos and even iconic films. Finally, our model can support HDR synthesis pipelines built upon existing SDR generative video models. Output HDR videos can be viewed on our supplementary webpage: sdr2hdrvideo.github.io
Related papers
- DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models [35.73610036182625]
Diff is a framework that formulates LDR-to-generative conversion within the latent space of a video diffusion model.<n>Our framework further enables controllable LDR-to-video conversion guided by text or reference images.
arXiv Detail & Related papers (2026-04-07T17:56:18Z) - InstantHDR: Single-forward Gaussian Splatting for High Dynamic Range 3D Reconstruction [48.64335106961342]
Existing HDR pipelines rely on known camera poses, well-calibrated dense point clouds, and time-consuming per-scene optimization.<n>Current feed-forward alternatives overlook the HDR problem by assuming exposure-in appearance.<n>We propose Instant, a feed-forward network that reconstructs 3D HDR scenes from uncalibrated multi-exposure LDR collections in a single forward pass.
arXiv Detail & Related papers (2026-03-11T20:51:47Z) - Reconstructing 3D Scenes in Native High Dynamic Range [82.90064638813185]
We present the first method for 3D scene reconstruction that directly models native HDR observations.<n>We propose bf Native High dynamic range 3D Gaussian Splatting (NH-3DGS), which preserves the full dynamic range throughout the reconstruction pipeline.<n>We demonstrate on both synthetic and real multi-view HDR datasets that NH-3DGS significantly outperforms existing methods in reconstruction quality and dynamic range preservation.
arXiv Detail & Related papers (2025-11-17T02:33:31Z) - Diffusion-Promoted HDR Video Reconstruction [45.73396977607666]
High dynamic range (LDR) video reconstruction aims to generate HDR videos from low dynamic range (LDR) frames captured with alternating exposures.
Most existing works solely rely on the regression-based paradigm, leading to adverse effects such as ghosting artifacts and missing details in saturated regions.
We propose a diffusion-promoted method for HDR video reconstruction, termed HDR-V-Diff, which incorporates a diffusion model to capture the HDR distribution.
arXiv Detail & Related papers (2024-06-12T13:38:10Z) - HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting [76.5908492298286]
Existing HDR NVS methods are mainly based on NeRF.
They suffer from long training time and slow inference speed.
We propose a new framework, High Dynamic Range Gaussian Splatting (-GS)
arXiv Detail & Related papers (2024-05-24T00:46:58Z) - Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos [35.19716951217485]
We analyze the impact of tonemapping operators on the visual quality of streaming HDR videos.
We build the first large-scale subjectively open-source database of compressed tone-mapped HDR videos.
arXiv Detail & Related papers (2024-03-22T09:38:16Z) - Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation [54.64335350932855]
We propose a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging.
Our proposed Asyn system integrates the Dynamic Vision Sensors (DVS) with a set of LCD panels.
The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams.
arXiv Detail & Related papers (2024-03-14T13:45:09Z) - HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment [36.1179702443845]
We introduce HIDRO-VQA, a no-reference (NR) video quality assessment model designed to provide precise quality evaluations of High Dynamic Range () videos.
Our findings demonstrate that self-supervised pre-trained neural networks can be further fine-tuned in a self-supervised setting to achieve state-of-the-art performance.
Our algorithm can be extended to the Full Reference VQA setting, also achieving state-of-the-art performance.
arXiv Detail & Related papers (2023-11-18T12:33:19Z) - Snapshot HDR Video Construction Using Coded Mask [25.12198906401246]
This study utilize 3D-CNNs to perform a joint demosaicking, denoising, and HDR video reconstruction from coded LDR video.
The obtained results are promising and could lead to affordable HDR video capture using conventional cameras.
arXiv Detail & Related papers (2021-12-05T09:32:11Z) - HDRVideo-GAN: Deep Generative HDR Video Reconstruction [19.837271879354184]
We propose an end-to-end GAN-based framework for HDR video reconstruction from LDR sequences with alternating exposures.
We first extract clean LDR frames from noisy LDR video with alternating exposures with a denoising network trained in a self-supervised setting.
We then align the neighboring alternating-exposure frames to a reference frame and then reconstruct high-quality HDR frames in a complete adversarial setting.
arXiv Detail & Related papers (2021-10-22T14:02:03Z) - HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with
Large Motions [62.44802076971331]
We propose a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content.
arXiv Detail & Related papers (2020-07-03T11:42:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.