AI Tools for Simulation Data Construction

The frames that decide safety validation are the ones fleets meet least — heavy fog, a cyclist emerging from a blind spot, a construction detour. Waiting to capture them naturally can take forever. Instead of building scenes from scratch, edit the real driving footage you already have: turn a clear day into rain, add a new lane, or insert a sudden hazard at an intersection. The original video provides a physically correct skeleton — you only change the variables, amplifying one clip into a batch of training samples covering different conditions.

Models

Wan3.0videocamFREE · 30 sec / 90 daysTry Nowarrow_forwardVideo-to-video conditional generation — edit real footage with prompts plus scene conditions (depth / segmentation / HDMap / trajectories) to produce weather, lighting, and corner-case variants from a single drive.

Real-robot data is the most expensive data in AI — one teleoperator, one robotic arm, one trajectory at a time. Fine-tune a video generation model into a world-action model: given a current view and an instruction, it imagines the next few seconds of manipulation and outputs executable action sequences, expanding scarce real-robot data. Its physical common sense and temporal consistency come from the Wan backbone, and it is compact enough to fine-tune on a lab budget and fast enough to sit inside the control loop.

Models

Wan3.0videocamFREE · 30 sec / 90 daysTry Nowarrow_forwardAs the training backbone, Wan's weights bring physical common sense and temporal consistency — fine-tuned into a world-action model that turns generated experience into training data.