metadriverse/SimGen
Simulator-conditioned Driving Scene Generation
This helps autonomous driving engineers create diverse and realistic driving scenarios for training and testing self-driving car systems. You input a basic simulator layout, such as road topology and agent positions, and it generates high-fidelity, varied visual scenes that mimic real-world conditions. This is for researchers and engineers developing and validating autonomous driving technologies.
134 stars. No commits in the last 6 months.
Use this if you need to generate a wide array of realistic driving scenes from basic simulator configurations to improve the robustness of autonomous driving systems.
Not ideal if you are looking to process existing real-world video data or if your primary need is general image generation unrelated to driving simulation.
Stars
134
Forks
13
Language
Python
License
—
Category
Last pushed
Apr 14, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/metadriverse/SimGen"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
cswry/SeeSR
[CVPR2024] SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
JJLibra/SALAD-Pan
🤗 Official implementation for "SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for...
open-mmlab/mmgeneration
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.
Janspiry/Image-Super-Resolution-via-Iterative-Refinement
Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
hanjq17/Spectrum
[CVPR 2026] Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration