# TTB: Test-time Baking for Efficient Rendering of Decoder-only View Synthesis Models

**Authors:** Chin-Yang Lin, Ryo Hachiuma, Jaesung Choe, Min-Hung Chen, Yu-Chiang Frank Wang, Yen-Yu Lin, Wei-Chen Chiu, Yu-Lun Liu, Cheng Sun  
**Lab:** NVIDIA · **Published:** 12 Nov 2026 · **Upvotes:** 19  
**Links:** [arXiv](https://arxiv.org/abs/2611.17331) · [PDF](https://arxiv.org/pdf/2611.17331) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.17331.html)

## Key numbers

- **23×**: Our method achieves up to **23x** rendering speedup with quality on par with the state-of-the-art across…

## Abstract

Decoder-only Large View Synthesis Models (LVSMs) with KV-cache have recently achieved state-of-the-art quality by regressing novel views from neural networks without reconstructing 3D geometry, while Test-Time Training (TTT) layers save computation but sacrifice quality. We propose Test-Time Baking (TTB), a strategy that learns to bake global scene information from cross-view attention into a lightweight MLP, enabling fast and high-fidelity novel-view decoding. The first key is to repurpose the fast-weight MLP of TTT to learn the cross-view attention mapping explicitly from its input-output pairs, rather than replacing attention entirely, achieving the same rendering efficiency as TTT while delivering substantially higher quality. The second key addresses a modality misalignment between context prefilling and novel-view rendering: we disentangle context tokens into camera-only context rendering tokens to serve as TTB input, and context ground-truth tokens with image content to serve as the TTB mapping target, ensuring the baked MLPs are relieved of the modality generalization challenge between context and novel-view processing. Our method achieves up to **23x** rendering speedup with quality on par with the state-of-the-art across several benchmarks. Project page: https://test-time-baking.github.io

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