arXiv:2609.28300v1 Announce Type: new Abstract: Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the...
arXiv:2607. 03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks.
Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brightness-change observations with high temporal resolution, but prior works often treat voxel channels as an unordered or static feature stack before fusion, rather than explicitly modeling their within-window temporal evolution, weakening the temporal evidence that makes events useful.
arXiv:2607.08016v3 Announce Type: replace Abstract: Video relighting requires balancing long-form temporal consistency with a physically grounded understanding of light transport, which depends on ac...
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:2609.00901v1 Announce Type: new Abstract: Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely...
The paper introduces a benchmark called Shedding Light to evaluate how well generative image models understand and reproduce lighting. The benchmark tests models by asking them to inpaint a simple object, called a light probe, into real photographs and then compares the generated probe to the ground truth to assess lighting direction, colour, and radiance. The authors provide a scalable protocol and open-source code and data for systematic assessment of photometric accuracy in future models.
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.
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:2512.01495v4 Announce Type: replace Abstract: Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other...
arXiv:2609.01123v1 Announce Type: new Abstract: Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detaile...
MirrorDistill introduces an illumination‑aware latent distillation framework for low‑light image enhancement. It trains a lightweight student encoder‑decoder by aligning its intermediate features with clean‑domain targets generated by a teacher decoder, using feature mirroring and illumination‑aware weighting to emphasize underexposed regions. The method achieves state‑of‑the‑art performance on the LOL‑v2‑Real benchmark while maintaining the lowest computational complexity, and the code is released as open source.