Zhenzhen Weng
Papers
1
Total Citations
7
H-Index
1
About
Zhenzhen Weng is a researcher advancing the field of 3D human perception and computer vision, with a focus on making human mesh recovery robust and practical for real-world, "in-the-wild" settings. Her key research areas include domain adaptation, 3D pose estimation, and human shape modeling. Weng's most notable contribution is her work on "Domain Adaptive 3D Pose Augmentation for In-the-Wild Human Mesh Recovery," which tackles the critical challenge of scarce ground truth 3D mesh data for training. By developing a domain adaptation framework that leverages synthetic data and augments 3D poses, she enables models to generalize effectively to diverse, uncontrolled environments—a breakthrough for applications in entertainment, robotics, and healthcare. This work, published in 2022, has already garnered 7 citations, reflecting its growing influence. Weng’s research bridges the gap between lab-based 3D reconstruction and real-world deployment, offering scalable solutions that reduce reliance on expensive motion-capture setups. Her innovative approach to data augmentation and domain transfer positions her as a rising voice in making 3D human sensing accessible and accurate for everyday scenarios.
Research Focus
Key Achievements
Top Papers
- 1Domain Adaptive 3D Pose Augmentation for In-the-Wild Human Mesh Recovery7 citations · 2022