arXiv Computer Vision
Albedo Estimation via Latent Bridge Matching
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Albedo Estimation via Latent Bridge Matching
Recent advances in Intrinsic Image Decomposition (IID) have increasingly relied on generative models. However, progress remains limited by three key challenges: (a) insufficient physical consistency,...
arXiv Computer Vision
Sep 1
MVID: Feed-Forward Multi-View Intrinsic Image Decomposition
arXiv:2512.23667v3 Announce Type: replace Abstract: Intrinsic image decomposition aims to recover material and illumination factors from RGB observations, but real-world images entangle reflectance w...
arXiv Computer Vision
Sep 2
HELIOS: From midnight to noon, continuous outdoor urban scene relighting
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...
arXiv Computer Vision
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Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models
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.
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Real-Time Volumetric Light Transport Inference without a Path
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