arXiv Computer Vision
HDR Video Generation via Latent Alignment with Logarithmic Encoding
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Sep 2
DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models
arXiv:2604.06161v3 Announce Type: replace-cross Abstract: Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance i...
arXiv Computer Vision
Sep 4
Generating HDR Video from SDR Video
The paper presents a framework for converting standard dynamic range (SDR) videos into high dynamic range (HDR) videos using large-scale generative video models. It introduces a Multi-Exposure Video Model (MEVM) that predicts exposure-bracketed linear SDR sequences from a single nonlinear SDR input, and a Video Merging Model (VMM) that fuses these predictions into a high-quality HDR sequence while preserving detail in shadows and highlights. Experiments, qualitative evaluation, and a user study demonstrate robust HDR conversion for casual consumer footage and iconic films, and the approach can be integrated into existing SDR generative video pipelines.
arXiv Computer Vision
Sep 14
Recurrent Dynamic Range Extension
The paper introduces a method for progressively extending the dynamic range of an image by learning to increase it by a single exposure value first, then applying the network recurrently to achieve full HDR reconstruction. The approach is agnostic to input dynamic range, targets a bounded output domain, and utilizes RAW images with adversarial losses to produce realistic results. Memory Replay during backpropagation allows training over multiple inference stages, reducing reconstruction errors and enabling robust recovery of bright highlights in long‑tailed HDR scenes.
arXiv Computer Vision
Aug 25
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
arXiv Machine Learning
Jun 9
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
arXiv:2606. 09056v1 Announce Type: cross Abstract: Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths.
Hugging Face Trending Papers
Aug 13
V-RAE: Rethinking Video Latent Spaces for Generation
Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization.