# RenderBox: Expressive Performance Rendering with Text Control

arXiv: https://arxiv.org/abs/2502.07711 · HTML: https://mlsanity.com/paper/2502.07711/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [SyMuPe: Affective and Controllable Symbolic Music Performance](https://mlsanity.com/paper/2511.03425/) (2025)
- [EmoHeal: An End-to-End System for Personalized Therapeutic Music Retrieval from Fine-grained Emotions](https://mlsanity.com/paper/2509.15986/) (2025)
- [Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis](https://mlsanity.com/paper/2605.14555/) (2026)
- [PianoKontext: Expressive Performance Rendering from Deadpan Context](https://mlsanity.com/paper/2606.12282/) (2026)

## Abstract (arXiv)

Expressive music performance rendering involves interpreting symbolic scores with variations in timing, dynamics, articulation, and instrument-specific techniques, resulting in performances that capture musical can emotional intent. We introduce RenderBox, a unified framework for text-and-score controlled audio performance generation across multiple instruments, applying coarse-level controls through natural language descriptions and granular-level controls using music scores. Based on a diffusion transformer architecture and cross-attention joint conditioning, we propose a curriculum-based paradigm that trains from plain synthesis to expressive performance, gradually incorporating controllable factors such as speed, mistakes, and style diversity. RenderBox achieves high performance compared to baseline models across key metrics such as FAD and CLAP, and also tempo and pitch accuracy under different prompting tasks. Subjective evaluation further demonstrates that RenderBox is able to generate controllable expressive performances that sound natural and musically engaging, aligning well with prompts and intent.