| title | SolarRenderer: Few-Step Generative Reference Rendering |
|---|---|
| authors | Guian Fang, Shengju Qian, Dongyue Lu, Junchao Huang, Lutao Jiang, Xin Wang, Yiren Song, Li Jiang, Mike Zheng Shou |
| date | 2026-10-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.28630 |
| affiliation | Show Lab |
| upvotes | 51 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Game engines and coding agents can specify the layout, motion, and camera of a scene, yet turning such coarse structure into realistic video requires a renderer that follows the structure and completes the appearance it leaves open. Existing renderers are tied to the representation of one engine, need tens of denoising steps or a costly distillation of their own, and learn from conditions that are not aligned with their target videos. We present SolarRenderer, a structure-conditioned video renderer that adapts a frozen MiniMax-H3 33B backbone with two lightweight LoRAs. Structural videos enter the native reference pathway of the backbone at the positions of the frames they describe, without a type identifier and described only in plain text, so that a single control LoRA follows arbitrary structural representations. Starting from a pretrained four-step LoRA, structure-guided distribution matching renders in four steps, 12.3 times faster than 50-step sampling, while preserving the learned control. A block-causal variant extends the same design to streaming and renders 768p video in real time. To train the model, we build a frame-aligned corpus of 136K clips from game engines, simulators, and YouTube videos. Experiments on an in-distribution test set and a generalization benchmark of 16 structural representations show that SolarRenderer achieves state-of-the-art results across both, and generalizes to 11 unseen representations, including pose skeletons, drawings, and point clouds. |
cite
@misc{fang2026solarrenderer,
title={SolarRenderer: Few-Step Generative Reference Rendering},
author={Guian Fang and Shengju Qian and Dongyue Lu and Junchao Huang and Lutao Jiang and Xin Wang and Yiren Song and Li Jiang and Mike Zheng Shou},
year={2026},
eprint={2610.28630},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.28630}
}