Zhenzhen (Jen) Weng

I am a fifth year Ph.D student in Computational and Mathematical Engineering (ICME) at Stanford University where I am advised by Prof. Serena Yeung.

I am broadly interested in 3D computer vision and machine learning. Specifically, my current research efforts are focused on developing cutting-edge deep learning algorithms for perceiving and modeling human pose and behavior in the 3D world. I am alo interested in applying my work towards developing AI-assisted healthcare solutions.

Prior to my Ph.D, I received B.S. in Computer Science and B.S. in Mathematics from Carnegie Mellon University. I also previously worked for a fund manager on the East Coast.

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Feb, 2023: 2 Papers accepted to CVPR 2023.

Jan, 2023: Joining Adobe Research as a Research Scientist Intern this summer.

Recent text-to-image generative models such as Stable Diffusion often struggle to preserve plausible human structure in the generations. We propose Diffusion model with Human Pose Correction (Diffusion-HPC), a method that generates photo-realistic images with plausible posed humans by injecting prior knowledge about human body structure. The generated image-mesh pairs are well-suited for downstream human mesh recovery task.

We propose GC-KPL - Geometry Consistency inspired Key Point Leaning. By training on the large WOD training set without any annotated keypoints, we attain reasonable performance as compared to the fully supervised approach. Further, the backbone benefits from the unsupervised training and is useful in downstream fewshot learning of keypoints, where fine-tuning on only 10 percent of the labeled training data gives comparable performance to fine-tuning on the entire set.

We aim to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action.

We propose Domain Adaptive 3D Pose Augmentation (DAPA), a data augmentation method that combines the strength of methods based on synthetic datasets by getting direct supervision from the synthesized meshes.

We propose a holistically trainable model that perceives the 3D scene from a single RGB image, estimates the camera pose and the room layout, and reconstructs both human body and object meshes.

We propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation, through learning instance embeddings of masked regions.

We introduce the challenge of improving slice-specific performance without damaging the overall model quality, and proposed the first programming abstraction and machine learning model to support these actions.

We introduced weak supervision heuristics as a methodology to infer complex objects and situations by combining simpler outputs from current, state-of-the art object detectors.

Research Scientist Intern @ Adobe Research, San Jose, CA, Jun - Sept, 2023

Research Intern (Perception) @ Waymo, Mountain View, CA, Jun - Nov, 2022

Machine Learning Engineer @ VMware, Palo Alto, CA, Jun - Sept, 2019

Research Engineer @ AQR, Greenwich, CT, 2016 - 2018

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