Despite the impressive progress on understanding and generating images shown by the recent unified architectures, the integration of 3D tasks remains challenging and largely unexplored. In this paper, we introduce UniUGG, the first unified understanding and generation framework for 3D modalities. Our unified framework employs an LLM to comprehend and decode sentences and 3D representations. At its core, we propose a spatial decoder leveraging a latent diffusion model to generate high-quality 3D representations. This allows for the generation and imagination of 3D scenes based on a reference image and an arbitrary view transformation, while remaining supports for spatial visual question answering (VQA) tasks. Additionally, we propose a geometric-semantic learning strategy to pretrain the vision encoder. This design jointly captures the input's semantic and geometric cues, enhancing both spatial understanding and generation. Extensive experimental results demonstrate the superiority of our method in visual representation, spatial understanding, and 3D generation.
@article{arxiv.2508.11952,
title = {UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding},
author = {Yueming Xu and Jiahui Zhang and Ze Huang and Yurui Chen and Yanpeng Zhou and Zhenyu Chen and Yu-Jie Yuan and Pengxiang Xia and Guowei Huang and Xinyue Cai and Zhongang Qi and Xingyue Quan and Jianye Hao and Hang Xu and Li Zhang},
journal= {arXiv preprint arXiv:2508.11952},
year = {2026}
}