Guanjun Wu (吴官骏)

I am a first-year Ph.D. student of Huazhong University of Science and Technology, School of CS. I got my bachelor degree from HUST in 2023. Under the supervised of Prof. Xinggang Wang, Prof. Wenyu Liu and with the guidance of Dr. Jiemin Fang in the School of EIC, I am enjoying my Ph.D. career in collaboration with HuaWei. My research interests mainly focus on 3D Vision/ Neural Rendering.

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My research interests are efficient neural rendering technology, including: HDR neural radiance fields, dynamic and static scene reconstruction, 3D Generation, etc.

* Equal contribution.
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
Guanjun Wu*, Taoran Yi*, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, Xinggang Wang
CVPR, 2024
[ Paper] [ Page] [ Code]

In 4D-GS, a novel explicit representation containing both 3D Gaussians and 4D neural voxels is proposed. A decomposed neural voxel encoding algorithm inspired by HexPlane is proposed to efficiently build Gaussian features from 4D neural voxels and then a lightweight MLP is applied to predict Gaussian deformations at novel timestamps. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800 X 800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state-of-the-art methods.

GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with Point Cloud Priors
Taoran Yi*, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, Xinggang Wang
CVPR, 2024
[ Paper] [ Page] [ Code]

A fast 3D generation framework, named as GaussianDreamer, is proposed, where the 3D diffusion model provides point cloud priors for initialization and the 2D diffusion model enriches the geometry and appearance. Our GaussianDreamer can generate a high-quality 3D instance within 25 minutes on one GPU, much faster than previous methods, while the generated instances can be directly rendered in real time.

Fast High Dynamic Range Radiance Fields for Dynamic Scenes
Guanjun Wu*, Taoran Yi*, Jiemin Fang, Wenyu Liu, Xinggang Wang
3DV, 2024
[ Paper] [ Page] [ Code] [ Data]

we propose a dynamic HDR NeRF framework, named as HDR-HexPlane, which can learn 3D scenes from dynamic 2D images captured with various exposures.With the proposed model, high-quality novel-view images at any time point can be rendered with any desired exposure. We further construct a dataset containing multiple dynamic scenes captured with diverse exposures for evaluation.

  • 11/2022 - 3/2023 SenseTime, ShangHai
  • 3/2022 - 10/2022 ByteDance, ShangHai

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Last updated: Feb. 2024