facebookresearch/ai4animationpy

A Python framework for AI-driven character animation using neural networks.

What it solves

AI4AnimationPy addresses the friction in AI-driven character animation research by unifying training, inference, and visualization into a single Python environment. Previously, researchers had to use disconnected tools—Python for model research and specialized software like Unity for visualization—which slowed iteration and made real-time validation difficult.

How it works

The framework is built on NumPy and PyTorch, removing the need for external game engines. It uses a game-engine-style architecture based on an Entity-Component-System (ECS) and update loops, allowing for modular extension. It can be run in three modes: Standalone (with built-in rendering), Headless (for server-side training), or Manual (for precise control over update intervals).

Who it’s for

It is designed for researchers and animation engineers working on neural network-based character animation, motion capture processing, and real-time skeletal mesh rendering.

Highlights

  • Unified Workflow: Combines training, inference, and rendering in one backend, enabling instant visualization of inputs and outputs during training.
  • Modular ECS Architecture: Allows easy extension with new features like physics, audio, or vision.
  • Comprehensive Tooling: Includes a real-time renderer with deferred shading, a FABRIK inverse kinematics solver, and support for importing motion capture data from GLB, FBX, and BVH formats.
  • PyTorch Integration: Full support for backpropagation through inference and quantization, which was not possible in previous Unity-dependent workflows.

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