arXiv Computer Vision
SyntheticDoc: A Large Synthetic Dataset for Document Unwarping and Illumination Correction
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 26
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
5d ago
Integrating Learned Priors through Prior-Space Reparameterization for Physically Based Inverse Rendering
The paper introduces a prior-guided material reparameterization for physically based inverse rendering, leveraging learned material predictions to restrict the optimization space without altering the photometric objective. By fusing multi-view predictions onto a mesh and constructing a permutohedral lattice in prior feature space, surface points with similar predictions share material variables, which are jointly optimized with geometry and illumination. Experiments on synthetic benchmarks and Stanford-ORB show improved material decomposition and relighting, with PSNR gains of 1.5–2.5 dB over strong baselines and a faster optimization schedule that maintains high relighting quality.
arXiv Computer Vision
Sep 4
PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models
PoseDreamer is a new pipeline that uses diffusion models to generate large‑scale synthetic datasets for 3D human mesh estimation, providing 3D mesh annotations that remain aligned with the generated images. The system incorporates controllable image generation, Direct Preference Optimization for control alignment, curriculum‑based hard sample mining, and multi‑stage quality filtering to produce over 500,000 high‑quality samples with a 76% improvement in image‑quality metrics over traditional rendering‑based datasets. Models trained on PoseDreamer match or surpass those trained on real‑world or conventional synthetic data, and combining PoseDreamer with synthetic datasets yields better performance than mixing real and synthetic data alone.
arXiv Computer Vision
Sep 11
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.
arXiv Computer Vision
Sep 15
Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents
arXiv:2609.14352v1 Announce Type: new Abstract: AI-generated image detection has attracted increasing attention, but existing evaluations mainly focus on natural images, leaving AI-generated document...
arXiv Machine Learning
Aug 12
InSight-doc: Agentic Visual Perception for Long-Document Understanding
arXiv:2608. 10628v1 Announce Type: cross Abstract: Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot.