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I am a second-year Eng.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 Eng.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: 4D Reconstruction, 3D Generation etc. * Equal contribution. |
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Shuai Zhang*, Guanjun Wu*, Xinggang Wang, Bin Feng, Wenyu Liu ArXiv, 2024 [ Paper] [ Code] we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, a high-quality dynamic mesh sequence of the object can be extracted. |
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Taoran Yi, Jiemin Fang, Zanwei Zhou, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Xinggang Wang, Qi Tian ArXiv, 2024 [ Paper] [ Page] [ Code] Aiming at highly enhancing the generation quality, we propose a novel framework named GaussianDreamerPro. The main idea is to bind Gaussians to reasonable geometry, which evolves over the whole generation process. Along different stages of our framework, both the geometry and appearance can be enriched progressively. The final output asset is constructed with 3D Gaussians bound to mesh, which shows significantly enhanced details and quality compared with previous methods. Notably, the generated asset can also be seamlessly integrated into downstream manipulation pipelines, e.g. animation, composition, and simulation etc., greatly promoting its potential in wide applications. |
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Guanjun Wu*, Taoran Yi*, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, Xinggang Wang CVPR, 2024 [ Paper] [ Page] [ Code] Ranked 2nd in CVPR paper influence, 2000+ GitHub stars. 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. |
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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. |
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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. |
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I really like playing games, including but not limited to:
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Template is adapted from
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