Human Motion Generation: A Survey

Human motion generation aims to generate natural human pose sequences and shows immense potential for real-world applications. Substantial progress has been made recently in motion data collection technologies and generation methods, laying the foundation for increasing interest in human motion generation. Most research within this field focuses on generating human motions based on conditional signals, such as text, audio, and scene contexts. While significant advancements have been made in recent years, the task continues to pose challenges due to the intricate nature of human motion and its implicit relationship with conditional signals. In this survey, we present a comprehensive literature review of human motion generation, which, to the best of our knowledge, is the first of its kind in this field. We begin by introducing the background of human motion and generative models, followed by an examination of representative methods for three mainstream sub-tasks: text-conditioned, audio-conditioned, and scene-conditioned human motion generation. Additionally, we provide an overview of common datasets and evaluation metrics. Lastly, we discuss open problems and outline potential future research directions. We hope that this survey could provide the community with a comprehensive glimpse of this rapidly evolving field and inspire novel ideas that address the outstanding challenges.Comment: 20 pages, 5 figure

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.Comment: Accepted to SIGGRAPH 201

Reconstructing Three-Dimensional Models of Interacting Humans

Understanding 3d human interactions is fundamental for fine-grained scene analysis and behavioural modeling. However, most of the existing models predict incorrect, lifeless 3d estimates, that miss the subtle human contact aspects--the essence of the event--and are of little use for detailed behavioral understanding. This paper addresses such issues with several contributions: (1) we introduce models for interaction signature estimation (ISP) encompassing contact detection, segmentation, and 3d contact signature prediction; (2) we show how such components can be leveraged to ensure contact consistency during 3d reconstruction; (3) we construct several large datasets for learning and evaluating 3d contact prediction and reconstruction methods; specifically, we introduce CHI3D, a lab-based accurate 3d motion capture dataset with 631 sequences containing $2,525$ contact events, $728,664$ ground truth 3d poses, as well as FlickrCI3D, a dataset of $11,216$ images, with $14,081$ processed pairs of people, and $81,233$ facet-level surface correspondences. Finally, (4) we propose methodology for recovering the ground-truth pose and shape of interacting people in a controlled setup and (5) annotate all 3d interaction motions in CHI3D with textual descriptions. Motion data in multiple formats (GHUM and SMPLX parameters, Human3.6m 3d joints) is made available for research purposes at \url{https://ci3d.imar.ro}, together with an evaluation server and a public benchmark

사람 동작의 마커없는 재구성

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 이제희.Markerless human pose recognition using a single-depth camera plays an important role in interactive graphics applications and user interface design. Recent pose recognition algorithms have adopted machine learning techniques, utilizing a large collection of motion capture data. The effectiveness of the algorithms is greatly influenced by the diversity and variability of training data. Many applications have been developed to use human body as a controller to utilize these pose recognition systems. In many cases, using general props help us perform immersion control of the system. Nevertheless, the human pose and prop recognition system is not yet sufficiently powerful. Moreover, there is a problem such as invisible parts lower the quality of human pose estimation from a single depth camera due to an absence of observed data. In this thesis, we present techniques to manipulate the human motion data for enabling to estimate human pose from a single depth camera. First, we developed method that resamples a collection of human motion data to improve the pose variability and achieve an arbitrary size and level of density in the space of human poses. The space of human poses is high-dimensional and thus brute-force uniform sampling is intractable. We exploit dimensionality reduction and locally stratified sampling to generate either uniform or application-specifically biased distributions in the space of human poses. Our algorithm is learned to recognize such challenging poses such as sit, kneel, stretching and yoga using a remarkably small amount of training data. The recognition algorithm can also be steered to maximize its performance for a specific domain of human poses. We demonstrate that our algorithm performs much better than Kinect SDK for recognizing challenging acrobatic poses, while performing comparably for easy upright standing poses. Second, we find out environmental object which interact with human beings. We proposed a new props recognition system, which can applied on the existing human pose estimation algorithm, and enable to powerful props estimation with human poses at the same times. Our work is widely applicable to various types of controllers system, which deals with the human pose and addition items simultaneously. Finally, we enhance the pose estimation result. All the part of human body cannot be always estimated from the single depth image. In some case, some body parts are occluded by other body parts, and sometimes estimation system fail to success. For solving this problem, we construct novel neural network model which called autoencoder. It is constructed from huge natural pose data. Then it can reconstruct the missing parameter of human pose joint as new correct joint. It can be applied to many different human pose estimation systems to improve their performance.1 Introduction 1 2 Background 9 2.1 Research on Motion Data 9 2.2 Human Pose Estimation 10 2.3 Machine Learning on Human Pose Estimation 11 2.4 Dimension Reduction and Uniform Sampling 12 2.5 Neural Networks on Motion Data 13 3 Markerless Human Pose Recognition System 14 3.1 System Overview 14 3.2 Preprocessing Data Process 15 3.3 Randomized Decision Tree 20 3.4 Joint Estimation Process 22 4 Controllable Sampling Data in the Space of Human Poses 26 4.1 Overview 26 4.2 Locally Stratified Sampling 28 4.3 Experimental Results 34 4.4 Discussion 40 5 Human Pose Estimation with Interacting Prop from Single Depth Image 48 5.1 Introduction 48 5.2 Prop Estimation 50 5.3 Experimental Results 53 5.4 Discussion 57 6 Enhancing the Estimation of Human Pose from Incomplete Joints 58 6.1 Overview 58 6.2 Method 59 6.3 Experimental Result 62 6.4 Discussion 66 7 Conclusion 67 Bibliography 69 초록 81Docto