Hello! Iā€™m Peng Zheng (郑鹏 in Chinese), currently pursuing my first year of a PhD program at Jilin University, where I am also involved in joint training at the Shanghai Innovation Institute. I am a member of the Intelligent Content Learning (ICL) group within the School of Artificial Intelligence at Jilin University. My research interests lie in generative models and neural rendering.

I have the privilege of being advised by Associate Professor Rui Ma. My journey in the field of artificial intelligence has been exciting, and Iā€™m passionate about exploring novel ways to create and understand visual content.

Feel free to connect with me or explore my work further! šŸ˜Š

šŸ”„ News

  • 2024.08: One paper is accepted by PG 2024. Thanks to Ruiqi Liu!
  • 2024.07: One paper is accepted by ECCV 2024.
  • 2024.03: Create my homepage.

šŸ“ Publications

ECCV 2024
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SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation

Peng Zheng, Tao Liu, Zili Yi, Rui Ma

  • We introduce SemanticHuman-HD, the first method to achieve semantic disentangled human image synthesis. Notably, SemanticHuman-HD is also the first method to achieve 3D-aware image synthesis at 10242 resolution, benefiting from our proposed 3D-aware super-resolution module.
PG 2024
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3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis

Ruiqi Liu, Peng Zheng, Ye Wang, Rui Ma

  • We propose 3D-SSGAN, a novel framework for 3D-aware compositional portrait image synthesis. First, a simple yet effective depth-guided 2D-to-3D lifting module maps the generated 2D part features and semantics to 3D. Then, a volume renderer with a novel 3D-aware semantic mask renderer is utilized to produce the composed face features and corresponding masks.
arXiv2024
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TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization

Renyi Mao, Qingshan Xu, Peng Zheng, Ye Wang, Tieru Wu, Rui Ma

  • In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.
arXiv2024
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D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes

Jiaxing Zhao, Peng Zheng, Rui Ma

  • We propose D-Aug, a LiDAR data augmentation method tailored for augmenting dynamic scenes. D-Aug extracts objects and inserts them into dynamic scenes, considering the continuity of these objects across consecutive frames.

šŸŽ– Honors and Awards

  • 2023: Received a third prize in the Second Jittor Artificial Intelligence Competition.

šŸ“– Educations

  • 2022.09 - 2025.06, M.E., Jilin University, Computer Science and Technology.
  • 2018.09 - 2022.06, B.E., Jilin University, Agricultural Mechanization and Automation.