Skip to main content
zenodoopen

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data

<p>Data accompanying our paper on:&nbsp;<em>Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data</em></p> <p>&nbsp;</p> <p><a href="https://github.com/tlpss/diffusing-synthetic-data" target="_blank" rel="noopener">github repository</a></p> <p>meshes.zip contains the 3D meshes used to generate the synthetic data for all three object categories</p> <p>real-datasets.zip contains the real-world image datasets gathered to evaluate the synthetic data</p> <p>&nbsp;</p> <p>&nbsp;</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0