SPLATIFY: Reproduce, Discover, Innovate! From Papers and Ideas to Trainable 3DGS Code
We introduce SPLATIFY, a multi-agent framework that converts 3DGS papers into trainable gsplat-based implementations, where generic paper-to-code methods and frontier models fail.
Key points
- The rapid growth of 3D Gaussian Splatting (3DGS) research demands significant effort to reimplement papers before building on them.
- SPLATIFY achieves this through five innovations: (1) A context-free grammar for gsplat over a modular method template with extension points for losses, densification, rendering, and optimization, constraining synthesis so generated code satisfies gsplat's architectural invariants by construction.
- (2) Architectural elements for faithful reproduction: fork-aware citation recovery retrieving component-level code at function-level granularity, Graph-of-Thought synthesis in topological dependency order, RAG-guided in-context example selection from over 20 verified implementations, and visual feedback combining PSNR-guided regeneration, Gaussian-level structural checks, and VLM-driven patching.
- (5) SPLATIFY-Bench, an evaluation framework across 30 diverse 3DGS papers.
Sources (1)
- [1]SPLATIFY: Reproduce, Discover, Innovate! From Papers and Ideas to Trainable 3DGS CodearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 09:06 PM
We introduce SPLATIFY, a multi-agent framework that converts 3DGS papers into trainable gsplat-based implementations, where generic paper-to-code methods and frontier models fail.
The rapid growth of 3D Gaussian Splatting (3DGS) research demands significant effort to reimplement papers before building on them.
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