AMAP-ML/FE2E
[CVPR 2026] Beyond Generation: Advancing Image Editing Priors for Depth and Normal Estimation
Leverages a DiT-based architecture adapted from advanced image editing models (Step1X-Edit) via LoRA fine-tuning to perform monocular depth and surface normal estimation with zero-shot generalization. Achieves dense geometry prediction by transferring editing priors to geometry tasks, supporting multiple benchmark datasets (NYU-v2, KITTI, ScanNet, ETH3D) through unified inference pipelines with multi-GPU evaluation support.
195 stars.
Stars
195
Forks
7
Language
Python
License
MIT
Category
Last pushed
Mar 17, 2026
Commits (30d)
0
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