arXiv Computer Vision
HELIOS: From midnight to noon, continuous outdoor urban scene relighting
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Jul 15
Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data
Reliable perception under diverse weather conditions remains a major challenge for autonomous driving systems. A common strategy to improve robustness is either to synthesize adverse weather conditions for training perception models or to apply weather-removal techniques to recover clean inputs.
arXiv Computer Vision
Oct 9
PAM-ToD: Plug-and-Play Appearance Modeling for Cross-Time-of-Day 3D Gaussian Splatting
PAM-ToD is a lightweight plug‑in that adapts a pre‑trained 3D Gaussian Splatting road scene to a new time of day by learning color corrections while keeping the original 3DGS parameters fixed. It scales each Gaussian’s existing color to model illumination changes and adds a brightness term for effects such as street lamps, learning these corrections from a few anchor images without separately estimating albedo and illumination. The method includes a spatial regularization to avoid abrupt changes and is evaluated on the CARLA-ToD benchmark, achieving higher PSNR and lower LPIPS than baselines in both static and dynamic settings.
arXiv Computer Vision
2d ago
Do Image Editing Models Understand Lighting?
arXiv:2606.26738v3 Announce Type: replace Abstract: While recent advancements in generative image editing models have achieved stunning visual fidelity, it remains an open question whether these syst...
arXiv Computer Vision
Oct 1
Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky
arXiv:2512.06174v3 Announce Type: replace Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-...
arXiv Computer Vision
6d ago
JointLight: Human-Centric Video Relighting with Asynchronous RGB-Irradiance Joint Modeling
JointLight introduces an asynchronous joint-modeling approach for human-centric video relighting, enabling fine-grained illumination control by jointly denoising RGB and irradiance frames during two-stage training. The method employs a domain matching strategy to bridge gaps between synthetic irradiance cues and real irradiance maps, and uses an asynchronous hijacked generation pipeline at inference. A newly created multi-illumination synthetic video dataset with paired irradiance maps supports training, and the model demonstrates strong generalization to real-world scenarios, outperforming prior methods in lighting fidelity and generative quality.
arXiv Computer Vision
Sep 1
Di$^2$CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schr\"odinger Bridge Transformer
arXiv:2608.29043v1 Announce Type: new Abstract: Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and dec...