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...
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.
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...
GLOW is a Global Illumination‑aware inverse rendering framework for indoor scenes captured with dynamic co‑located light and camera setups. It combines a neural implicit surface representation with a neural radiance cache to jointly optimize geometry and reflectance, while introducing a dynamic radiance cache and a surface‑angle‑weighted radiometric loss to handle near‑field motion, strong inter‑reflections, and specular highlights. Experiments demonstrate that GLOW significantly outperforms prior methods in estimating material reflectance under both natural and co‑located illumination.
Editable 3D scene creation requires object instances and lights that can be inspected, moved, and imported into standard engines, yet existing single-image methods largely stop at room-scale geometry, baked/global illumination, or text-driven generation. We introduce Lumera (Light-aware Unified Engine-native Reconstruction and Assembly), a benchmark and reference pipeline for engine-native, light-aware 3D scene parsing from a single image.
arXiv:2502. 07531v5 Announce Type: replace-cross Abstract: Controllable image-to-video (I2V) generation transforms a reference image into a coherent video guided by user-specified control signals.
arXiv:2601.01865v2 Announce Type: replace Abstract: With the growing demand for real-time video enhancement in live applications, existing methods often struggle to balance speed and effective exposu...
arXiv:2606. 22574v2 Announce Type: replace-cross Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables.
The paper introduces a method that combines casual indoor photographs taken with and without flash to recover geometry, materials, and lighting. By using flash residuals to constrain albedo and BRDF and static lighting to capture specular highlights, the approach employs a hash‑encoded MLP anchored to a 2DGS depth map for view‑consistent material decomposition. Experiments on synthetic and real scenes show improved diffuse color, albedo, roughness, and relighting performance, achieving a 4.17 dB PSNR gain over the best baseline.
arXiv:2606. 06899v1 Announce Type: cross Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and within environments.
arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light...
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-...