|
Sheng Miao (苗升) Zhejiang University. Hangzhou, PRC. I received my Ph.D. degree from Zhejiang University in September 2026, where I was advised by Prof. Yiyi Liao. Prior to this, I completed my Master's degree in Control Science and Engineering at Northwestern Polytechnical University (NWPU), advised by Prof. Xiaoxiong Liu. I received the Bachelor's degree from NWPU in 2019. I have focused on projects involving feed-forward techniques for 3D/4D scene reconstruction and 3D generation. I serve as a conference reviewer for CVPR, ECCV, NeurIPS, and ICLR. I will soon join Huawei. Feel free to contact me at shengmiao@zju.edu.cn. |
|
News |
|
Selected Publications* means equal contribution. |
|
|
EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
Sheng Miao, Sijin Li, Dongfeng Bai, Pan Wang, Bingbing Liu, Yue Wang, Andreas Geiger, Yiyi Liao, International Journal of Computer Vision (IJCV), 2026 project page / arXiv A unified feed-forward 3D Gaussian Splatting framework for static & dynamic urban scenes that achieves real-time rendering speeds. Completes scene reconstruction in ~1.3 seconds with photo-realistic quality. |
|
ScenDi: 3D-to-2D Scene Diffusion Cascades for Urban Generation
Hanlei Guo, Jiahao Shao, Xinya Chen, Xiyang Tan, Sheng Miao, Yujun Shen, Yiyi Liao, CVPR, 2026 project page / arXiv A 3D-to-2D scene diffusion cascade that combines controllable 3D Gaussian generation with 2D video diffusion for high-quality urban scene synthesis. |
|
FreeFix: Boosting 3D Gaussian Splatting via Fine-Tuning-Free Diffusion Models
Hongyu Zhou, Zisen Shao, Sheng Miao, Pan Wang, Dongfeng Bai, Bingbing Liu, Yiyi Liao, 3DV, 2026 arXiv A method designed to improve the rendering results of extrapolated views in 3D Gaussian Splatting, without requiring fine-tuning of diffusion models.. |
|
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu∗, Hongyu Zhou∗, Sida Peng, Haotong Lin, Haoyu Guo, Jiahao Shao, Peishan Yang, Qinglin Yang, Sheng Miao, Xingyi He, Yifan Wang, Yue Wang, Ruizhen Hu, Yiyi Liao, Xiaowei Zhou, Hujun Bao, Computational Visual Media, 2026 Preprint This paper surveys the evolution of deep learning architectures and paradigms for depth estimation across the monocular, stereo, multi-view, and monocular video settings. We explore the potential of these models to address existing challenges and provide a comprehensive overview of large-scale datasets that can facilitate their development. |
|
|
Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting
Jiaxin Huang, Sheng Miao, Bangbang Yang, Yuewen Ma, Yiyi Liao, ICCV, 2025 project page / arXiv A generative framework that addresses the challenge of reconstructing dynamic scenes from casual monocular videos with video diffusion model. |
|
EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis
Sheng Miao, Jiaxin Huang, Dongfeng Bai, Xu Yan, Hongyu Zhou, Yue Wang, Bingbing Liu, Andreas Geiger, Yiyi Liao, CVPR, 2025 project page / arXiv A generalizable framework that predicts 3D Gaussians across multiple frames within a unified volume using convolutional networks. |
|
|
Efficient Depth-Guided Urban View Synthesis
Sheng Miao∗, Jiaxin Huang∗, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Andreas Geiger, Yiyi Liao, ECCV, 2024 project page / arXiv A generalizable NeRF model that explicitly leverages geometric priors for feed-forward inference on unbounded sparse urban scenes. |
|
A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information
Sheng Miao, Xiaoxiong Liu, Dazheng Wei, Changze Li, ISPRS International Journal of Geo-Information, 2021, SCI Journal A method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation to tackle dynamics. |
|
© 2026 Sheng Miao. Published with GitHub Pages, powered by Jon Barron. Source code for this website can be found here. |