An Inference Zoo for Real-Time Media Arts: Avendish as a Bridge Between AI and Creative Environments

Jean-Michaël Celerier

Track: Paper

Keywords: AI Inference, Creative Coding, Real-Time Systems, Media Arts, C++, ONNXRuntime, Digital Preservation

TL;DR: This paper presents an open-source C++ framework that packages AI models into dependency-free, high-performance libraries, enabling seamless real-time inference in creative arts software without the complexities of a Python environment.

Abstract: We present an extension of Avendish to the model inference domain. Our project uses an open-source C++ library to democratize real-time AI inference in real-time media arts environments by providing a unified interface to deploy contemporary machine learning models without the complexity of Python dependencies. Through an abstraction layer built on onnxruntime, Avendish enables artists to compile models into single, portable C++ libraries that integrate seamlessly with creative coding environments. The library currently supports 15 production-ready models spanning computer vision (BlazePose, DepthAnything2, YOLO variants), style transfer (StyleGAN, AnimeGAN family), emotion recognition, and language models (Qwen3, FastVLM). Model selection was informed by in-situ analysis at a major media arts research center, identifying the most requested AI capabilities among projects for two years. We demonstrate the library's effectiveness through its integration in ossia score and discuss how this approach addresses critical challenges in creative AI: reducing technical barriers, ensuring use in real-time contexts, and providing long-term preservability of artistic works that depend on AI models.

Submission Number: 119