NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code

• URL: http://arxiv.org/abs/2603.00805v1
• Date: Sat, 28 Feb 2026 20:57:32 GMT
• Title: NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code
• Authors: Seemandhar Jain, Keshav Gupta, Kunal Gupta, Manmohan Chandraker,
• Abstract summary: We introduce NERFIFY, a framework that reliably converts NeRF research papers into trainable Nerfstudio plugins.<n>Code, data and implementations will be publicly released.
• Score: 49.610331036334316
• License: http://creativecommons.org/licenses/by/4.0/
• 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, proposal networks) from citation graphs of references. (4) Visual feedback: Artifacts are diagnosed through PSNR-minima ROI analysis, cross-view geometric validation, and VLM-guided patching to iteratively improve quality. (5) Knowledge enhancement: Beyond reproduction, methods can be improved with novel optimizations. (6) Benchmarking: An evaluation framework is designed for NeRF paper-to-code synthesis across 30 diverse papers. On papers without public implementations, NERFIFY achieves visual quality matching expert human code (+/-0.5 dB PSNR, +/-0.2 SSIM) while reducing implementation time from weeks to minutes. NERFIFY demonstrates that a domain-aware design enables code translation for complex vision papers, potentiating accelerated and democratized reproducible research. Code, data and implementations will be publicly released.

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