동영상 속 사람 동작의 물리 기반 재구성 및 분석

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 이제희.In computer graphics, simulating and analyzing human movement have been interesting research topics started since the 1960s. Still, simulating realistic human movements in a 3D virtual world is a challenging task in computer graphics. In general, motion capture techniques have been used. Although the motion capture data guarantees realistic result and high-quality data, there is lots of equipment required to capture motion, and the process is complicated. Recently, 3D human pose estimation techniques from the 2D video are remarkably developed. Researchers in computer graphics and computer vision have attempted to reconstruct the various human motions from video data. However, existing methods can not robustly estimate dynamic actions and not work on videos filmed with a moving camera. In this thesis, we propose methods to reconstruct dynamic human motions from in-the-wild videos and to control the motions. First, we developed a framework to reconstruct motion from videos using prior physics knowledge. For dynamic motions such as backspin, the poses estimated by a state-of-the-art method are incomplete and include unreliable root trajectory or lack intermediate poses. We designed a reward function using poses and hints extracted from videos in the deep reinforcement learning controller and learned a policy to simultaneously reconstruct motion and control a virtual character. Second, we simulated figure skating movements in video. Skating sequences consist of fast and dynamic movements on ice, hindering the acquisition of motion data. Thus, we extracted 3D key poses from a video to then successfully replicate several figure skating movements using trajectory optimization and a deep reinforcement learning controller. Third, we devised an algorithm for gait analysis through video of patients with movement disorders. After acquiring the patients joint positions from 2D video processed by a deep learning network, the 3D absolute coordinates were estimated, and gait parameters such as gait velocity, cadence, and step length were calculated. Additionally, we analyzed the optimization criteria of human walking by using a 3D musculoskeletal humanoid model and physics-based simulation. For two criteria, namely, the minimization of muscle activation and joint torque, we compared simulation data with real human data for analysis. To demonstrate the effectiveness of the first two research topics, we verified the reconstruction of dynamic human motions from 2D videos using physics-based simulations. For the last two research topics, we evaluated our results with real human data.컴퓨터 그래픽스에서 인간의 움직임 시뮬레이션 및 분석은 1960 년대부터 다루어진 흥미로운 연구 주제이다. 몇 십년 동안 활발하게 연구되어 왔음에도 불구하고, 3차원 가상 공간 상에서 사실적인 인간의 움직임을 시뮬레이션하는 연구는 여전히 어렵고 도전적인 주제이다. 그동안 사람의 움직임 데이터를 얻기 위해서 모션 캡쳐 기술이 사용되어 왔다. 모션 캡처 데이터는 사실적인 결과와 고품질 데이터를 보장하지만 모션 캡쳐를 하기 위해서 필요한 장비들이 많고, 그 과정이 복잡하다. 최근에 2차원 영상으로부터 사람의 3차원 자세를 추정하는 연구들이 괄목할 만한 결과를 보여주고 있다. 이를 바탕으로 컴퓨터 그래픽스와 컴퓨터 비젼 분야의 연구자들은 비디오 데이터로부터 다양한 인간 동작을 재구성하려는 시도를 하고 있다. 그러나 기존의 방법들은 빠르고 다이나믹한 동작들은 안정적으로 추정하지 못하며 움직이는 카메라로 촬영한 비디오에 대해서는 작동하지 않는다. 본 논문에서는 비디오로부터 역동적인 인간 동작을 재구성하고 동작을 제어하는 방법을 제안한다. 먼저 사전 물리학 지식을 사용하여 비디오에서 모션을 재구성하는 프레임 워크를 제안한다. 공중제비와 같은 역동적인 동작들에 대해서 최신 연구 방법을 동원하여 추정된 자세들은 캐릭터의 궤적을 신뢰할 수 없거나 중간에 자세 추정에 실패하는 등 불완전하다. 우리는 심층강화학습 제어기에서 영상으로부터 추출한 포즈와 힌트를 활용하여 보상 함수를 설계하고 모션 재구성과 캐릭터 제어를 동시에 하는 정책을 학습하였다. 둘 째, 비디오에서 피겨 스케이팅 기술을 시뮬레이션한다. 피겨 스케이팅 기술들은 빙상에서 빠르고 역동적인 움직임으로 구성되어 있어 모션 데이터를 얻기가 까다롭다. 비디오에서 3차원 키 포즈를 추출하고 궤적 최적화 및 심층강화학습 제어기를 사용하여 여러 피겨 스케이팅 기술을 성공적으로 시연한다. 셋 째, 파킨슨 병이나 뇌성마비와 같은 질병으로 인하여 움직임 장애가 있는 환자의 보행을 분석하기 위한 알고리즘을 제안한다. 2차원 비디오로부터 딥러닝을 사용한 자세 추정기법을 사용하여 환자의 관절 위치를 얻어낸 다음, 3차원 절대 좌표를 얻어내어 이로부터 보폭, 보행 속도와 같은 보행 파라미터를 계산한다. 마지막으로, 근골격 인체 모델과 물리 시뮬레이션을 이용하여 인간 보행의 최적화 기준에 대해 탐구한다. 근육 활성도 최소화와 관절 돌림힘 최소화, 두 가지 기준에 대해 시뮬레이션한 후, 실제 사람 데이터와 비교하여 결과를 분석한다. 처음 두 개의 연구 주제의 효과를 입증하기 위해, 물리 시뮬레이션을 사용하여 이차원 비디오로부터 재구성한 여러 가지 역동적인 사람의 동작들을 재현한다. 나중 두 개의 연구 주제는 사람 데이터와의 비교 분석을 통하여 평가한다.1 Introduction 1 2 Background 9 2.1 Pose Estimation from 2D Video . . . . . . . . . . . . . . . . . . . . 9 2.2 Motion Reconstruction from Monocular Video . . . . . . . . . . . . 10 2.3 Physics-Based Character Simulation and Control . . . . . . . . . . . 12 2.4 Motion Reconstruction Leveraging Physics . . . . . . . . . . . . . . 13 2.5 Human Motion Control . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.1 Figure Skating Simulation . . . . . . . . . . . . . . . . . . . 16 2.6 Objective Gait Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.7 Optimization for Human Movement Simulation . . . . . . . . . . . . 17 2.7.1 Stability Criteria . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Human Dynamics from Monocular Video with Dynamic Camera Movements 19 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 Pose and Contact Estimation . . . . . . . . . . . . . . . . . . . . . . 21 3.4 Learning Human Dynamics . . . . . . . . . . . . . . . . . . . . . . . 24 3.4.1 Policy Learning . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4.2 Network Training . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.3 Scene Estimator . . . . . . . . . . . . . . . . . . . . . . . . 29 3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.1 Video Clips . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.2 Comparison of Contact Estimators . . . . . . . . . . . . . . . 33 3.5.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.5.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Figure Skating Simulation from Video 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Skating Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Non-holonomic Constraints . . . . . . . . . . . . . . . . . . 46 4.3.2 Relaxation of Non-holonomic Constraints . . . . . . . . . . . 47 4.4 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.5 Trajectory Optimization and Control . . . . . . . . . . . . . . . . . . 50 4.5.1 Trajectory Optimization . . . . . . . . . . . . . . . . . . . . 50 4.5.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5 Gait Analysis Using Pose Estimation Algorithm with 2D-video of Patients 61 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.2.1 Patients and video recording . . . . . . . . . . . . . . . . . . 63 5.2.2 Standard protocol approvals, registrations, and patient consents 66 5.2.3 3D Pose estimation from 2D video . . . . . . . . . . . . . . . 66 5.2.4 Gait parameter estimation . . . . . . . . . . . . . . . . . . . 67 5.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . 68 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.3.1 Validation of video-based analysis of the gait . . . . . . . . . 68 5.3.2 gait analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.4.1 Validation with the conventional sensor-based method . . . . 75 5.4.2 Analysis of gait and turning in TUG . . . . . . . . . . . . . . 75 5.4.3 Correlation with clinical parameters . . . . . . . . . . . . . . 76 5.4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Supplementary Material . . . . . . . . . . . . . . . . . . . . . . . . . 77 6 Control Optimization of Human Walking 80 6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.1 Musculoskeletal model . . . . . . . . . . . . . . . . . . . . . 82 6.2.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.3 Control co-activation level . . . . . . . . . . . . . . . . . . . 83 6.2.4 Push-recovery experiment . . . . . . . . . . . . . . . . . . . 84 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7 Conclusion 90 7.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Docto

Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a reinforcement learning framework for training a robot to accomplish a large variety of jumping tasks, such as jumping to different locations and directions. To improve performance on these challenging tasks, we develop a new policy structure that encodes the robot's long-term input/output (I/O) history while also providing direct access to a short-term I/O history. In order to train a versatile jumping policy, we utilize a multi-stage training scheme that includes different training stages for different objectives. After multi-stage training, the policy can be directly transferred to a real bipedal Cassie robot. Training on different tasks and exploring more diverse scenarios lead to highly robust policies that can exploit the diverse set of learned maneuvers to recover from perturbations or poor landings during real-world deployment. Such robustness in the proposed policy enables Cassie to succeed in completing a variety of challenging jump tasks in the real world, such as standing long jumps, jumping onto elevated platforms, and multi-axes jumps.Comment: Accepted in Robotics: Science and Systems 2023 (RSS 2023). The accompanying video is at https://youtu.be/aAPSZ2QFB-

Training Physics-based Controllers for Articulated Characters with Deep Reinforcement Learning

In this thesis, two different applications are discussed for using machine learning techniques to train coordinated motion controllers in arbitrary characters in absence of motion capture data. The methods highlight the resourcefulness of physical simulations to generate synthetic and generic motion data that can be used to learn various targeted skills. First, we present an unsupervised method for learning loco-motion skills in virtual characters from a low dimensional latent space which captures the coordination between multiple joints. We use a technique called motor babble, wherein a character interacts with its environment by actuating its joints through uncoordinated, low-level (motor) excitation, resulting in a corpus of motion data from which a manifold latent space can be extracted. Using reinforcement learning, we then train the character to learn locomotion (such as walking or running) in the low-dimensional latent space instead of the full-dimensional joint action space. The thesis also presents an end-to-end automated framework for training physics-based characters to rhythmically dance to user-input songs. A generative adversarial network (GAN) architecture is proposed that learns to generate physically stable dance moves through repeated interactions with the environment. These moves are then used to construct a dance network that can be used for choreography. Using DRL, the character is then trained to perform these moves, without losing balance and rhythm, in the presence of physical forces such as gravity and friction

Expressive movement generation with machine learning

Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion