From Editor to Dense Geometry Estimator

1. 1Leverage visual priors from pre-trained models
2. 2Fine-tune image editing models for dense geometry estimation
3. 3Compare fine-tuning behaviors of editors and generators
4. 4Apply the approach to various dense prediction tasks

Computer vision engineers and researchers can benefit from this approach as it provides a more suitable foundation for dense prediction tasks, and can be applied to various applications such as robotics and autonomous vehicles

💡 Image editing models can be a more suitable foundation for fine-tuning than pre-trained text-to-image generative models for dense prediction tasks

📸 Fine-tuning image editors can outperform text-to-image generators for dense geometry estimation! 💡