Search Results for author: Zhenzhen Weng

Found 11 papers, 5 papers with code

Multi-Human Mesh Recovery with Transformers

no code implementations26 Feb 2024 Zeyu Wang, Zhenzhen Weng, Serena Yeung-Levy

Conventional approaches to human mesh recovery predominantly employ a region-based strategy.

Human Mesh Recovery

Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM

no code implementations22 Jan 2024 Zhenzhen Weng, Jingyuan Liu, Hao Tan, Zhan Xu, Yang Zhou, Serena Yeung-Levy, Jimei Yang

We present Human-LRM, a diffusion-guided feed-forward model that predicts the implicit field of a human from a single image.

3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels

no code implementations CVPR 2023 Zhenzhen Weng, Alexander S. Gorban, Jingwei Ji, Mahyar Najibi, Yin Zhou, Dragomir Anguelov

We show that by training on a large training set from Waymo Open Dataset without any human annotated keypoints, we are able to achieve reasonable performance as compared to the fully supervised approach.

ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image

no code implementations25 May 2023 Zhenzhen Weng, Zeyu Wang, Serena Yeung

Recent advancements in text-to-image generation have enabled significant progress in zero-shot 3D shape generation.

3D Shape Generation Image to 3D +1

Diffusion-HPC: Synthetic Data Generation for Human Mesh Recovery in Challenging Domains

1 code implementation16 Mar 2023 Zhenzhen Weng, Laura Bravo-Sánchez, Serena Yeung-Levy

Recent text-to-image generative models have exhibited remarkable abilities in generating high-fidelity and photo-realistic images.

Human Mesh Recovery Synthetic Data Generation

NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action

no code implementations CVPR 2023 Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, João Pedro Araújo, Jeffrey Gu, Karen Liu, Serena Yeung

Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.

3D Reconstruction Human Mesh Recovery +1

NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action

1 code implementation28 Dec 2022 Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, Joao Pedro Araujo, Jeffrey Gu, C. Karen Liu, Serena Yeung

Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.

3D Reconstruction Human Mesh Recovery +1

Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery

1 code implementation21 Jun 2022 Zhenzhen Weng, Kuan-Chieh Wang, Angjoo Kanazawa, Serena Yeung

The ability to perceive 3D human bodies from a single image has a multitude of applications ranging from entertainment and robotics to neuroscience and healthcare.

Data Augmentation Domain Adaptation +1

Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision

no code implementations CVPR 2021 Zhenzhen Weng, Mehmet Giray Ogut, Shai Limonchik, Serena Yeung

Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks.

Instance Segmentation Novel Object Detection +2

Holistic 3D Human and Scene Mesh Estimation from Single View Images

1 code implementation CVPR 2021 Zhenzhen Weng, Serena Yeung

Indeed, from a single image of a person placed in an indoor scene, we as humans are adept at resolving ambiguities of the human pose and room layout through our knowledge of the physical laws and prior perception of the plausible object and human poses.

Indoor Scene Reconstruction Object

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

2 code implementations NeurIPS 2019 Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré

In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes.

Autonomous Driving BIG-bench Machine Learning

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