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I am a Ph.D. student in the School of Software at Tsinghua University, supervised by Prof. Yu-Shen Liu. I started my Ph.D. in September 2026 and received my Master's degree in Software Engineering from the same school in 2026. My research interests center on physical AI. I aim to build world models that enable intelligent agents to understand the physical world, anticipate the consequences of their actions, and interact with it effectively. |
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(* Equal Contribution) |
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Weiqi Zhang, Wenyuan Zhang, Yu-Shen Liu, Junsheng Zhou Conference on Neural Information Processing Systems (NeurIPS), 2026 video Unifies video generation and 3D world modeling by decoding video latents into explicit 4D Gaussians, enabling real-time rendering across viewpoints and timesteps. |
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Weiqi Zhang*, Junsheng Zhou*, Xuancheng Zhang, Juntong Fang, Zequn Chen, Donglin Di, Yu-Shen Liu Conference on Neural Information Processing Systems (NeurIPS), 2026 video Reconstructs dynamic 4D Gaussian scenes from monocular videos in seconds, jointly modeling geometry, appearance, and motion for real-time novel view synthesis. |
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Weiqi Zhang*, Junsheng Zhou*, Juntong Fang, Kanle Shi, Shenkun Xu, Yu-Shen Liu Conference on Neural Information Processing Systems (NeurIPS), 2026 video Generates native 4D Gaussian Splatting from image and text conditions, using temporally structured latents to separate geometry from appearance. |
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Weiqi Zhang*, Junsheng Zhou*, Haotian Geng, Kanle Shi, Shenkun Xu, Yi Fang, Yu-Shen Liu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026 project page | arXiv | code A geometry-aware framework that grows 3D Gaussians from point clouds with text guidance for controllable 3D generation. |
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Juntong Fang*, Zequn Chen*, Weiqi Zhang*, Donglin Di, Xuancheng Zhang, Chengmin Yang, Yu-Shen Liu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026 (Highlight) project page | arXiv | code A feed-forward 4D reconstruction network that efficiently recovers dynamic 3D scenes from monocular videos by disentangling dynamic motion from static structure with attention-forcing and grouped causal attention. |
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Junsheng Zhou*, Weiqi Zhang*, Baorui Ma, Kanle Shi, Yu-Shen Liu, Zhizhong Han IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026 project page | arXiv | code A unified framework providing large-scale datasets, benchmarks, and generative models for UDF-based 3D shape representation and generation. |
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Wenyuan Zhang*, Jimin Tang*, Weiqi Zhang, Yi Fang, Yu-Shen Liu, Zhizhong Han Advances in Neural Information Processing Systems (NeurIPS), 2025 project page | arXiv | code We introduce MaterialRefGS that significantly improves the rendering and reconstruction of reflective scenes in Gaussian Splatting by introducing PBR-based multi-view material consistency constraints and reflection strength prior. |
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Weiqi Zhang*, Junsheng Zhou*, Haotian Geng*, Wenyuan Zhang, Yu-Shen Liu IEEE/CVF International Conference on Computer Vision, (ICCV), 2025 project page | arXiv | code GAP gaussianizes raw point clouds into high-fidelity 3D Gaussians with text guidance via depth-aware diffusion and surface-anchored optimization. |
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Takeshi Noda*, Chao Chen*, Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu, Zhizhong Han IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025 project page | arXiv | code Bijective-SDF improves 3D surface reconstruction from sparse point clouds by learning bijective surface parameterization and optimizing grid deformation. |
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Junsheng Zhou*, Weiqi Zhang*, Yu-Shen Liu Conference on Neural Information Processing Systems (NeurIPS), 2024 project page | arXiv | code DiffGS is a powerful and efficient 3D generative model capable of generating Gaussian primitives in arbitrary numbers, with a functional disentanglement of Gaussian Splatting. |
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Takeshi Noda*, Chao Chen*, Weiqi Zhang, Xinhai Liu, Yu-Shen Liu, Zhizhong Han Conference on Neural Information Processing Systems (NeurIPS), 2024 project page | arXiv | code MultiPull is a novel method that improves 3D surface reconstruction from point clouds by optimizing multi-scale implicit fields and leveraging frequency features. |
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Junsheng Zhou*, Weiqi Zhang*, Baorui Ma, Kanle Shi, Yu-Shen Liu, Zhizhong Han IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 project page | arXiv | code We present UDiFF, a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally. |
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