NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code
Seemandhar Jain, Keshav Gupta, Kunal Gupta, Manmohan Chandraker

TL;DR
NERFIFY is a domain-specific multi-agent framework that automates the conversion of NeRF research papers into executable code, significantly reducing implementation time and improving reproducibility.
Contribution
It introduces a novel multi-agent system with domain-aware constraints for reliable paper-to-code translation in NeRF research.
Findings
Achieves visual quality matching expert human code (+/-0.5 dB PSNR)
Reduces implementation time from weeks to minutes
Demonstrates domain-specific design enables complex vision paper translation
Abstract
The proliferation of neural radiance field (NeRF) research requires significant efforts to reimplement papers before building upon them. We introduce NERFIFY, a multi-agent framework that reliably converts NeRF research papers into trainable Nerfstudio plugins, in contrast to generic paper-to-code methods and frontier models like GPT-5 that usually fail to produce runnable code. NERFIFY achieves domain-specific executability through six key innovations: (1) Context-free grammar (CFG): LLM synthesis is constrained by Nerfstudio formalized as a CFG, ensuring generated code satisfies architectural invariants. (2) Graph-of-Thought code synthesis: Specialized multi-file-agents generate repositories in topological dependency order, validating contracts and errors at each node. (3) Compositional citation recovery: Agents automatically retrieve and integrate components (samplers, encoders,…
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Taxonomy
TopicsScientific Computing and Data Management · Machine Learning in Materials Science · Model-Driven Software Engineering Techniques
