Search Results for author: Hanbyul Joo

Found 23 papers, 13 papers with code

Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion

no code implementations18 Apr 2022 Evonne Ng, Hanbyul Joo, Liwen Hu, Hao Li, Trevor Darrell, Angjoo Kanazawa, Shiry Ginosar

We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of corresponding listener motion.

BANMo: Building Animatable 3D Neural Models from Many Casual Videos

1 code implementation23 Dec 2021 Gengshan Yang, Minh Vo, Natalia Neverova, Deva Ramanan, Andrea Vedaldi, Hanbyul Joo

Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model.

3D Shape Reconstruction 3D Shape Reconstruction from Videos

Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics

no code implementations2 Dec 2021 Yingdong Qian, Marta Kryven, Tao Gao, Hanbyul Joo, Josh Tenenbaum

We describe Generative Body Kinematics model, which predicts human intention inference in this domain using Bayesian inverse planning and inverse body kinematics.

Ego4D: Around the World in 3,000 Hours of Egocentric Video

no code implementations13 Oct 2021 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification

D3D-HOI: Dynamic 3D Human-Object Interactions from Videos

2 code implementations19 Aug 2021 Xiang Xu, Hanbyul Joo, Greg Mori, Manolis Savva

We evaluate this approach on our dataset, demonstrating that human-object relations can significantly reduce the ambiguity of articulated object reconstructions from challenging real-world videos.

Human-Object Interaction Detection

FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration

1 code implementation13 Aug 2021 Yu Rong, Takaaki Shiratori, Hanbyul Joo

Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert of subtle movements of face, hands, and body.

3D Human Reconstruction 3D Pose Estimation

FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration

1 code implementation19 Aug 2020 Yu Rong, Takaaki Shiratori, Hanbyul Joo

To construct FrankMocap, we build the state-of-the-art monocular 3D "hand" motion capture method by taking the hand part of the whole body parametric model (SMPL-X).

3D Hand Pose Estimation 3D Human Reconstruction +1

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

1 code implementation ECCV 2020 Jason Y. Zhang, Sam Pepose, Hanbyul Joo, Deva Ramanan, Jitendra Malik, Angjoo Kanazawa

We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment.

3D Human Pose Estimation 3D Shape Reconstruction From A Single 2D Image +2

Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics

no code implementations CVPR 2021 Evonne Ng, Shiry Ginosar, Trevor Darrell, Hanbyul Joo

We demonstrate the efficacy of our method on hand gesture synthesis from body motion input, and as a strong body prior for single-view image-based 3D hand pose estimation.

3D Hand Pose Estimation

Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation

1 code implementation7 Apr 2020 Hanbyul Joo, Natalia Neverova, Andrea Vedaldi

Remarkably, the resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks such as 3DPW.

3D Human Pose Estimation 3D Pose Estimation

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

3 code implementations CVPR 2020 Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo

Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.

3D Human Pose Estimation 3D Human Reconstruction +3

Single-Network Whole-Body Pose Estimation

2 code implementations ICCV 2019 Gines Hidalgo, Yaadhav Raaj, Haroon Idrees, Donglai Xiang, Hanbyul Joo, Tomas Simon, Yaser Sheikh

We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints.

Multi-Task Learning Pose Estimation

Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction

1 code implementation CVPR 2019 Hanbyul Joo, Tomas Simon, Mina Cikara, Yaser Sheikh

We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of social signals humans use.

You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions

1 code implementation CVPR 2020 Evonne Ng, Donglai Xiang, Hanbyul Joo, Kristen Grauman

The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera.

Pose Estimation

Capture Dense: Markerless Motion Capture Meets Dense Pose Estimation

no code implementations5 Dec 2018 Xiu Li, Yebin Liu, Hanbyul Joo, Qionghai Dai, Yaser Sheikh

Specifically, we first introduce a novel markerless motion capture method that can take advantage of dense parsing capability provided by the dense pose detector.

Human Parsing Markerless Motion Capture +1

Structure from Recurrent Motion: From Rigidity to Recurrency

no code implementations CVPR 2018 Xiu Li, Hongdong Li, Hanbyul Joo, Yebin Liu, Yaser Sheikh

This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM) from a long monocular video sequence observing a non-rigid object performing recurrent and possibly repetitive dynamic action.

Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

no code implementations CVPR 2018 Hanbyul Joo, Tomas Simon, Yaser Sheikh

We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures.

Panoptic Studio: A Massively Multiview System for Social Interaction Capture

1 code implementation9 Dec 2016 Hanbyul Joo, Tomas Simon, Xulong Li, Hao liu, Lei Tan, Lin Gui, Sean Banerjee, Timothy Godisart, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, Yaser Sheikh

The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.

MAP Visibility Estimation for Large-Scale Dynamic 3D Reconstruction

no code implementations CVPR 2014 Hanbyul Joo, Hyun Soo Park, Yaser Sheikh

Many traditional challenges in reconstructing 3D motion, such as matching across wide baselines and handling occlusion, reduce in significance as the number of unique viewpoints increases.

3D Reconstruction

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