Lizhen Wang (王立祯)
I was advised by Yebin Liu in Institute for Brain and Cognitive Sciences. My research interests lie in 3D body reconstruction, 3D face reconstruction, 3D face tracking, StyleGAN-based or NeRF or 3DGS-based facial avatar and diffusion-based human generation.
E-mail: wanglz14@126.com
Wechat: wanglz14
Aug. 2014 - Jul. 2018, Mathematics and Physics, Tsinghua University,
Bachelor of Science.
Aug. 2018 - Jun. 2023, Department of Automation, Tsinghua University,
Ph.D in Yebin Liu's lab.
Jul. 2023 - Jul. 2024, Department of Automation, Tsinghua University,
Postdoc in Yebin Liu's lab.
Jul. 2024 - Now, Data-IC-CV-Virtual Human&AR, ByteDance
Algorithm Engineer.
May. 2020 - Jul. 2021 & Jul. 2022 - Sep. 2022, Alipay Business Line, Ant Group
Research Internship.
Jul. 2017 - Sep. 2017, Graphics & AI Lab, The University of Texas at Austin
Summer Internship.
3D Gaussian Parametric Head Model
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InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars
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MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar
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Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling
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RAM-Avatar: Real-time Photo-Realistic Avatar from Monocular Videos with Full-body Control
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Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians
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DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
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HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field
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StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video
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AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels
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LatentAvatar: Learning Latent Expression Code for Expressive Neural Head Avatar
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Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
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FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
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IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis
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NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image
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DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs
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