GitHub - puffinsoft/mousecrack: Human mouse imitation with deep learning.

Synthesize organically varied, human-like mouse movement.

This project aims to test the abilities of deep-learning for mouse imitation.

See it in Action

Clearly, it's not perfect. But this is v0.1.0. Still a lot of fun stuff to try on the model side :).

Installation

Usage

Available as an SDK (for developers) and CLI (for agents).

SDK

import { move, steps } from 'mousecrack';

await move(200, 400);

// or alternatively...
const from = { x: 100, y: 200 }
const to = { x: 200, y: 400 }
await steps(from, to);

// [
//   { x: 100, y: 200, t: 0 },
//   { x: 95, y: 202, t: 10.528131778472712 },
//   { x: 90, y: 210, t: 21.040190062833986 },
//   { x: 81, y: 223, t: 31.892832399406224 },
//   ...

CLI

mousecrack move 200 400 # (x, y)
mousecrack steps 100 200 200 400 # from (x, y), to (x, y)

/plugin marketplace add puffinsoft/mousecrack
/plugin install move-mouse@mousecrack

codex plugin marketplace add puffinsoft/mousecrack
codex plugin add move-mouse@mousecrack

How does it work?

Mousecrack treats mouse prediction like a time forecasting problem.

It models mouse movement as a change in position (dx, dy) and time (dt), and tries to predict the next step in this multivariate time series.

To avoid the mode collapse problem, Mousecrack uses a Mixture Density Network to model several trajectories as a probability distribution.

Mousecrack is open source software, licensed under the MIT license.