Direct Prediction of 3D Body Poses from Motion Compensated Sequences

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Previous approaches typically compute candidate poses in individual frames and then link them in a post-processing step to resolve ambiguities. By contrast, we directly regress from a spatio-temporal volume of bounding boxes to a 3D pose in the central frame. We further show that, for this approach to achieve its full potential, it is essential to compensate for the motion in consecutive frames so that the subject remains centered. This then allows us to effectively overcome ambiguities and improve upon the state-of-the-art by a large margin on the Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation benchmarks

3D 손 포즈 인식을 위한 인조 데이터의 이용

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021.8. 양한열.3D hand pose estimation (HPE) based on RGB images has been studied for a long time. Relevant methods have focused mainly on optimization of neural framework for graphically connected finger joints. Training RGB-based HPE models has not been easy to train because of the scarcity on RGB hand pose datasets; unlike human body pose datasets, the finger joints that span hand postures are structured delicately and exquisitely. Such structure makes accurately annotating each joint with unique 3D world coordinates difficult, which is why many conventional methods rely on synthetic data samples to cover large variations of hand postures. Synthetic dataset consists of very precise annotations of ground truths, and further allows control over the variety of data samples, yielding a learning model to be trained with a large pose space. Most of the studies, however, have performed frame-by-frame estimation based on independent static images. Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human hand pose estimation is a particularly interesting example of this synthetic-to-real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this dissertation, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images. We propose a novel method that generates a synthetic dataset that mimics natural human hand movements by re-engineering annotations of an extant static hand pose dataset into pose-flows. With the generated dataset, we train a newly proposed recurrent framework, exploiting visuo-temporal features from sequential images of synthetic hands in motion and emphasizing temporal smoothness of estimations with a temporal consistency constraint. Our novel training strategy of detaching the recurrent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust estimations. We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand pose estimation benchmarks. Since a fixed set of dataset provides a finite distribution of data samples, the generalization of a learning pose estimation network is limited in terms of pose, RGB and viewpoint spaces. We further propose to augment the data automatically such that the augmented pose sampling is performed in favor of training pose estimators generalization performance. Such auto-augmentation of poses is performed within a learning feature space in order to avoid computational burden of generating synthetic sample for every iteration of updates. The proposed effort can be considered as generating and utilizing synthetic samples for network training in the feature space. This allows training efficiency by requiring less number of real data samples, enhanced generalization power over multiple dataset domains and estimation performance caused by efficient augmentation.2D 이미지에서 사람의 손 모양과 포즈를 인식하고 구현흐는 연구는 각 손가락 조인트들의 3D 위치를 검출하는 것을 목표로한다. 손 포즈는 손가락 조인트들로 구성되어 있고 손목 관절부터 MCP, PIP, DIP 조인트들로 사람 손을 구성하는 신체적 요소들을 의미한다. 손 포즈 정보는 다양한 분야에서 활용될수 있고 손 제스쳐 감지 연구 분야에서 손 포즈 정보가 매우 훌륭한 입력 특징 값으로 사용된다. 사람의 손 포즈 검출 연구를 실제 시스템에 적용하기 위해서는 높은 정확도, 실시간성, 다양한 기기에 사용 가능하도록 가벼운 모델이 필요하고, 이것을 가능케 하기 위해서 학습한 인공신경망 모델을 학습하는데에는 많은 데이터가 필요로 한다. 하지만 사람 손 포즈를 측정하는 기계들이 꽤 불안정하고, 이 기계들을 장착하고 있는 이미지는 사람 손 피부 색과는 많이 달라 학습에 사용하기가 적절하지 않다. 그러기 때문에 본 논문에서는 이러한 문제를 해결하기 위해 인공적으로 만들어낸 데이터를 재가공 및 증량하여 학습에 사용하고, 그것을 통해 더 좋은 학습성과를 이루려고 한다. 인공적으로 만들어낸 사람 손 이미지 데이터들은 실제 사람 손 피부색과는 비슷할지언정 디테일한 텍스쳐가 많이 달라, 실제로 인공 데이터를 학습한 모델은 실제 손 데이터에서 성능이 현저히 많이 떨어진다. 이 두 데이타의 도메인을 줄이기 위해서 첫번째로는 사람손의 구조를 먼저 학습 시키기위해, 손 모션을 재가공하여 그 움직임 구조를 학스한 시간적 정보를 뺀 나머지만 실제 손 이미지 데이터에 학습하였고 크게 효과를 내었다. 이때 실제 사람 손모션을 모방하는 방법론을 제시하였다. 두번째로는 두 도메인이 다른 데이터를 네트워크 피쳐 공간에서 align시켰다. 그뿐만아니라 인공 포즈를 특정 데이터들로 augment하지 않고 네트워크가 많이 보지 못한 포즈가 만들어지도록 하나의 확률 모델로서 설정하여 그것에서 샘플링하는 구조를 제안하였다. 본 논문에서는 인공 데이터를 더 효과적으로 사용하여 annotation이 어려운 실제 데이터를 더 모으는 수고스러움 없이 인공 데이터들을 더 효과적으로 만들어 내는 것 뿐만 아니라, 더 안전하고 지역적 특징과 시간적 특징을 활용해서 포즈의 성능을 개선하는 방법들을 제안했다. 또한, 네트워크가 스스로 필요한 데이터를 찾아서 학습할수 있는 자동 데이터 증량 방법론도 함께 제안하였다. 이렇게 제안된 방법을 결합해서 더 나은 손 포즈의 성능을 향상 할 수 있다.1. Introduction 1 2. Related Works 14 3. Preliminaries: 3D Hand Mesh Model 27 4. SeqHAND: RGB-sequence-based 3D Hand Pose and Shape Estimation 31 5. Hand Pose Auto-Augment 66 6. Conclusion 85 Abstract (Korea) 101 감사의 글 103박

Exploiting temporal information for 3D pose estimation

In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately $12.2\%$ and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails

Unsupervised 3D Pose Estimation with Geometric Self-Supervision

We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data, 3D skeletons, correspondences between 2D-3D points, or use previously learned 3D priors during training. A lifting network accepts 2D landmarks as inputs and generates a corresponding 3D skeleton estimate. During training, the recovered 3D skeleton is reprojected on random camera viewpoints to generate new "synthetic" 2D poses. By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera view, we can define self-consistency loss both in 3D and in 2D. The training can thus be self supervised by exploiting the geometric self-consistency of the lift-reproject-lift process. We show that self-consistency alone is not sufficient to generate realistic skeletons, however adding a 2D pose discriminator enables the lifter to output valid 3D poses. Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data. This improves results and demonstrates the usefulness of 2D pose data for unsupervised 3D lifting. Results on Human3.6M dataset for 3D human pose estimation demonstrate that our approach improves upon the previous unsupervised methods by 30% and outperforms many weakly supervised approaches that explicitly use 3D data