zenodoopen
Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data
<p>Data accompanying our paper on: <em>Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data</em></p> <p> </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> </p> <p> </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