AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being able to study this incredible agility will be critical for the development of next-generation autonomous legged robots. In particular, the cheetah (acinonyx jubatus) is supremely fast and maneuverable, yet quantifying its whole-body 3D kinematic data during locomotion in the wild remains a challenge, even with new deep learning-based methods. In this work we present an extensive dataset of free-running cheetahs in the wild, called AcinoSet, that contains 119,490 frames of multi-view synchronized high-speed video footage, camera calibration files and 7,588 human-annotated frames. We utilize markerless animal pose estimation to provide 2D keypoints. Then, we use three methods that serve as strong baselines for 3D pose estimation tool development: traditional sparse bundle adjustment, an Extended Kalman Filter, and a trajectory optimization-based method we call Full Trajectory Estimation. The resulting 3D trajectories, human-checked 3D ground truth, and an interactive tool to inspect the data is also provided. We believe this dataset will be useful for a diverse range of fields such as ecology, neuroscience, robotics, biomechanics as well as computer vision.Comment: Code and data can be found at: https://github.com/African-Robotics-Unit/AcinoSe

단일 이미지로부터 여러 사람의 표현적 전신 3D 자세 및 형태 추정

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 이경무.Human is the most centric and interesting object in our life: many human-centric techniques and studies have been proposed from both industry and academia, such as motion capture and human-computer interaction. Recovery of accurate 3D geometry of human (i.e., 3D human pose and shape) is a key component of the human-centric techniques and studies. With the rapid spread of cameras, a single RGB image has become a popular input, and many single RGB-based 3D human pose and shape estimation methods have been proposed. The 3D pose and shape of the whole body, which includes hands and face, provides expressive and rich information, including human intention and feeling. Unfortunately, recovering the whole-body 3D pose and shape is greatly challenging; thus, it has been attempted by few works, called expressive methods. Instead of directly solving the expressive 3D pose and shape estimation, the literature has been developed for recovery of the 3D pose and shape of each part (i.e., body, hands, and face) separately, called part-specific methods. There are several more simplifications. For example, many works estimate only 3D pose without shape because additional 3D shape estimation makes the problem much harder. In addition, most works assume a single person case and do not consider a multi-person case. Therefore, there are several ways to categorize current literature; 1) part-specific methods and expressive methods, 2) 3D human pose estimation methods and 3D human pose and shape estimation methods, and 3) methods for a single person and methods for multiple persons. The difficulty increases while the outputs of methods become richer by changing from part-specific to expressive, from 3D pose estimation to 3D pose and shape estimation, and from a single person case to multi-person case. This dissertation introduces three approaches towards expressive 3D multi-person pose and shape estimation from a single image; thus, the output can finally provide the richest information. The first approach is for 3D multi-person body pose estimation, the second one is 3D multi-person body pose and shape estimation, and the final one is expressive 3D multi-person pose and shape estimation. Each approach tackles critical limitations of previous state-of-the-art methods, thus bringing the literature closer to the real-world environment. First, a 3D multi-person body pose estimation framework is introduced. In contrast to the single person case, the multi-person case additionally requires camera-relative 3D positions of the persons. Estimating the camera-relative 3D position from a single image involves high depth ambiguity. The proposed framework utilizes a deep image feature with the camera pinhole model to recover the camera-relative 3D position. The proposed framework can be combined with any 3D single person pose and shape estimation methods for 3D multi-person pose and shape. Therefore, the following two approaches focus on the single person case and can be easily extended to the multi-person case by using the framework of the first approach. Second, a 3D multi-person body pose and shape estimation method is introduced. It extends the first approach to additionally predict accurate 3D shape while its accuracy significantly outperforms previous state-of-the-art methods by proposing a new target representation, lixel-based 1D heatmap. Finally, an expressive 3D multi-person pose and shape estimation method is introduced. It integrates the part-specific 3D pose and shape of the above approaches; thus, it can provide expressive 3D human pose and shape. In addition, it boosts the accuracy of the estimated 3D pose and shape by proposing a 3D positional pose-guided 3D rotational pose prediction system. The proposed approaches successfully overcome the limitations of the previous state-of-the-art methods. The extensive experimental results demonstrate the superiority of the proposed approaches in both qualitative and quantitative ways.인간은 우리의 일상생활에서 가장 중심이 되고 흥미로운 대상이다. 그에 따라 모션 캡처, 인간-컴퓨터 인터렉션 등 많은 인간중심의 기술과 학문이 산업계와 학계에서 제안되었다. 인간의 정확한 3D 기하 (즉, 인간의 3D 자세와 형태)를 복원하는 것은 인간중심 기술과 학문에서 가장 중요한 부분 중 하나이다. 카메라의 빠른 대중화로 인해 단일 이미지는 많은 알고리즘의 널리 쓰이는 입력이 되었고, 그로 인해 많은 단일 이미지 기반의 3D 인간 자세 및 형태 추정 알고리즘이 제안되었다. 손과 발을 포함한 전신의 3D 자세와 형태는 인간의 의도와 느낌을 포함한 표현적이고 풍부한 정보를 제공한다. 하지만 전신의 3D 자세와 형태를 복원하는 것은 매우 어렵기 때문에 오직 극소수의 방법만이 이를 풀기 위해 제안되었고, 이를 위한 방법들을 표현적인 방법이라고 부른다. 표현적인 3D 자세와 형태를 한 번에 복원하는 것 대신, 사람의 몸, 손, 그리고 얼굴의 3D 자세와 형태를 따로 복원하는 방법들이 제안되었다. 이러한 방법들을 부분 특유 방법이라고 부른다. 이러한 문제의 간단화 이외에도 몇 가지의 간단화가 더 존재한다. 예를 들어, 많은 방법은 3D 형태를 제외한 3D 자세만을 추정한다. 이는 추가적인 3D 형태 추정이 문제를 더 어렵게 만들기 때문이다. 또한, 대부분의 방법은 오직 단일 사람의 경우만 고려하고 여러 사람의 경우는 고려하지 않는다. 그러므로, 현재 제안된 방법들은 몇 가지 기준에 의해 분류될 수 있다; 1) 부분 특유 방법 vs. 표현적 방법, 2) 3D 자세 추정 방법 vs. 3D 자세 및 형태 추정 방법, 그리고 3) 단일 사람을 위한 방법 vs. 여러 사람을 위한 방법. 부분 특유에서 표현적으로, 3D 자세 추정에서 3D 자세 및 형태 추정으로, 단일 사람에서 여러 사람으로 갈수록 추정이 더 어려워지지만, 더 풍부한 정보를 출력할 수 있게 된다. 본 학위논문은 단일 이미지로부터 여러 사람의 표현적인 3D 자세 및 형태 추정을 향하는 세 가지의 접근법을 소개한다. 따라서 최종적으로 제안된 방법은 가장 풍부한 정보를 제공할 수 있다. 첫 번째 접근법은 여러 사람을 위한 3D 자세 추정이고, 두 번째는 여러 사람을 위한 3D 자세 및 형태 추정이고, 그리고 마지막은 여러 사람을 위한 표현적인 3D 자세 및 형태 추정을 위한 방법이다. 각 접근법은 기존 방법들이 가진 중요한 한계점들을 해결하여 제안된 방법들이 실생활에서 쓰일 수 있도록 한다. 첫 번째 접근법은 여러 사람을 위한 3D 자세 추정 프레임워크이다. 단일 사람의 경우와는 다르게 여러 사람의 경우 사람마다 카메라 상대적인 3D 위치가 필요하다. 카메라 상대적인 3D 위치를 단일 이미지로부터 추정하는 것은 매우 높은 깊이 모호성을 동반한다. 제안하는 프레임워크는 심층 이미지 피쳐와 카메라 핀홀 모델을 사용하여 카메라 상대적인 3D 위치를 복원한다. 이 프레임워크는 어떤 단일 사람을 위한 3D 자세 및 형태 추정 방법과 합쳐질 수 있기 때문에, 다음에 소개될 두 접근법은 오직 단일 사람을 위한 3D 자세 및 형태 추정에 초점을 맞춘다. 다음에 소개될 두 접근법에서 제안된 단일 사람을 위한 방법들은 첫 번째 접근법에서 소개되는 여러 사람을 위한 프레임워크를 사용하여 쉽게 여러 사람의 경우로 확장할 수 있다. 두 번째 접근법은 여러 사람을 위한 3D 자세 및 형태 추정 방법이다. 이 방법은 첫 번째 접근법을 확장하여 정확도를 유지하면서 추가로 3D 형태를 추정하게 한다. 높은 정확도를 위해 릭셀 기반의 1D 히트맵을 제안하고, 이로 인해 기존에 발표된 방법들보다 큰 폭으로 높은 성능을 얻는다. 마지막 접근법은 여러 사람을 위한 표현적인 3D 자세 및 형태 추정 방법이다. 이것은 몸, 손, 그리고 얼굴마다 3D 자세 및 형태를 하나로 통합하여 표현적인 3D 자세 및 형태를 얻는다. 게다가, 이것은 3D 위치 포즈 기반의 3D 회전 포즈 추정기법을 제안함으로써 기존에 발표된 방법들보다 훨씬 높은 성능을 얻는다. 제안된 접근법들은 기존에 발표되었던 방법들이 갖는 한계점들을 성공적으로 극복한다. 광범위한 실험적 결과가 정성적, 정량적으로 제안하는 방법들의 효용성을 보여준다.1 Introduction 1 1.1 Background and Research Issues 1 1.2 Outline of the Dissertation 3 2 3D Multi-Person Pose Estimation 7 2.1 Introduction 7 2.2 Related works 10 2.3 Overview of the proposed model 13 2.4 DetectNet 13 2.5 PoseNet 14 2.5.1 Model design 14 2.5.2 Loss function 14 2.6 RootNet 15 2.6.1 Model design 15 2.6.2 Camera normalization 19 2.6.3 Network architecture 19 2.6.4 Loss function 20 2.7 Implementation details 20 2.8 Experiment 21 2.8.1 Dataset and evaluation metric 21 2.8.2 Experimental protocol 22 2.8.3 Ablation study 23 2.8.4 Comparison with state-of-the-art methods 25 2.8.5 Running time of the proposed framework 31 2.8.6 Qualitative results 31 2.9 Conclusion 34 3 3D Multi-Person Pose and Shape Estimation 35 3.1 Introduction 35 3.2 Related works 38 3.3 I2L-MeshNet 41 3.3.1 PoseNet 41 3.3.2 MeshNet 43 3.3.3 Final 3D human pose and mesh 45 3.3.4 Loss functions 45 3.4 Implementation details 47 3.5 Experiment 48 3.5.1 Datasets and evaluation metrics 48 3.5.2 Ablation study 50 3.5.3 Comparison with state-of-the-art methods 57 3.6 Conclusion 60 4 Expressive 3D Multi-Person Pose and Shape Estimation 63 4.1 Introduction 63 4.2 Related works 66 4.3 Pose2Pose 69 4.3.1 PositionNet 69 4.3.2 RotationNet 70 4.4 Expressive 3D human pose and mesh estimation 72 4.4.1 Body part 72 4.4.2 Hand part 73 4.4.3 Face part 73 4.4.4 Training the networks 74 4.4.5 Integration of all parts in the testing stage 74 4.5 Implementation details 77 4.6 Experiment 78 4.6.1 Training sets and evaluation metrics 78 4.6.2 Ablation study 78 4.6.3 Comparison with state-of-the-art methods 82 4.6.4 Running time 87 4.7 Conclusion 87 5 Conclusion and Future Work 89 5.1 Summary and Contributions of the Dissertation 89 5.2 Future Directions 90 5.2.1 Global Context-Aware 3D Multi-Person Pose Estimation 91 5.2.2 Unied Framework for Expressive 3D Human Pose and Shape Estimation 91 5.2.3 Enhancing Appearance Diversity of Images Captured from Multi-View Studio 92 5.2.4 Extension to the video for temporally consistent estimation 94 5.2.5 3D clothed human shape estimation in the wild 94 5.2.6 Robust human action recognition from a video 96 Bibliography 98 국문초록 111Docto

VIBE: Video Inference for Human Body Pose and Shape Estimation

Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose Video Inference for Body Pose and Shape Estimation (VIBE), which makes use of an existing large-scale motion capture dataset (AMASS) together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty is an adversarial learning framework that leverages AMASS to discriminate between real human motions and those produced by our temporal pose and shape regression networks. We define a temporal network architecture and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels. We perform extensive experimentation to analyze the importance of motion and demonstrate the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving state-of-the-art performance. Code and pretrained models are available at https://github.com/mkocabas/VIBE.Comment: CVPR-2020 camera ready. Code is available at https://github.com/mkocabas/VIB

Flowing ConvNets for Human Pose Estimation in Videos

The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps; (ii) spatial fusion layers that learn an implicit spatial model; (iii) optical flow is used to align heatmap predictions from neighbouring frames; and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also Chen & Yuille and Tompson et al. in the high precision region).Comment: ICCV'1

Semantic Graph Convolutional Networks for 3D Human Pose Regression

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.Comment: In CVPR 2019 (13 pages including supplementary material). The code can be found at https://github.com/garyzhao/SemGC