Real-time biped character stepping

PhD ThesisA rudimentary biped activity that is essential in interactive evirtual worlds, such as video-games and training simulations, is stepping. For example, stepping is fundamental in everyday terrestrial activities that include walking and balance recovery. Therefore an effective 3D stepping control algorithm that is computationally fast and easy to implement is extremely valuable and important to character animation research. This thesis focuses on generating real-time controllable stepping motions on-the-fly without key-framed data that are responsive and robust (e.g.,can remain upright and balanced under a variety of conditions, such as pushes and dynami- cally changing terrain). In our approach, we control the character’s direction and speed by means of varying the stepposition and duration. Our lightweight stepping model is used to create coordinated full-body motions, which produce directable steps to guide the character with specific goals (e.g., following a particular path while placing feet at viable locations). We also create protective steps in response to random disturbances (e.g., pushes). Whereby, the system automatically calculates where and when to place the foot to remedy the disruption. In conclusion, the inverted pendulum has a number of limitations that we address and resolve to produce an improved lightweight technique that provides better control and stability using approximate feature enhancements, for instance, ankle-torque and elongated-body

Real-Time Character Rise Motions

This paper presents an uncomplicated dynamic controller for generating physically-plausible three-dimensional full-body biped character rise motions on-the-fly at run-time. Our low-dimensional controller uses fundamental reference information (e.g., center-of-mass, hands, and feet locations) to produce balanced biped get-up poses by means of a real-time physically-based simulation. The key idea is to use a simple approximate model (i.e., similar to the inverted-pendulum stepping model) to create continuous reference trajectories that can be seamlessly tracked by an articulated biped character to create balanced rise-motions. Our approach does not use any key-framed data or any computationally expensive processing (e.g., offline-optimization or search algorithms). We demonstrate the effectiveness and ease of our technique through example (i.e., a biped character picking itself up from different laying positions)

Framework for embedding physical systems into virtual experiences

We present an immersive Virtual Reality (VR) experience through a combination of technologies including a physical rig; a gamelike experience; and a reï¬ned physics model with control. At its heart, the core technology introduces the concept of a physics-based communication that allows force-driven interaction to be shared between the player and game entities in the virtual world. Because the framework is generic and extendable, the application supports a myriad of interaction modes, constrained only by the limitations of the physical rig (see Figure 1). To showcase the technology, we demonstrate a locomoting robot placed in an immersive gamelike setting

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

A survey on human performance capture and animation

With the rapid development of computing technology, three-dimensional (3D) human body models and their dynamic motions are widely used in the digital entertainment industry. Human perfor- mance mainly involves human body shapes and motions. Key research problems include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical e�ects. In this survey, according to main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion sim- ulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animatio

Neural Categorical Priors for Physics-Based Character Control

Recent advances in learning reusable motion priors have demonstrated their effectiveness in generating naturalistic behaviors. In this paper, we propose a new learning framework in this paradigm for controlling physics-based characters with significantly improved motion quality and diversity over existing state-of-the-art methods. The proposed method uses reinforcement learning (RL) to initially track and imitate life-like movements from unstructured motion clips using the discrete information bottleneck, as adopted in the Vector Quantized Variational AutoEncoder (VQ-VAE). This structure compresses the most relevant information from the motion clips into a compact yet informative latent space, i.e., a discrete space over vector quantized codes. By sampling codes in the space from a trained categorical prior distribution, high-quality life-like behaviors can be generated, similar to the usage of VQ-VAE in computer vision. Although this prior distribution can be trained with the supervision of the encoder's output, it follows the original motion clip distribution in the dataset and could lead to imbalanced behaviors in our setting. To address the issue, we further propose a technique named prior shifting to adjust the prior distribution using curiosity-driven RL. The outcome distribution is demonstrated to offer sufficient behavioral diversity and significantly facilitates upper-level policy learning for downstream tasks. We conduct comprehensive experiments using humanoid characters on two challenging downstream tasks, sword-shield striking and two-player boxing game. Our results demonstrate that the proposed framework is capable of controlling the character to perform considerably high-quality movements in terms of behavioral strategies, diversity, and realism. Videos, codes, and data are available at https://tencent-roboticsx.github.io/NCP/

Skill learning based catching motion control

Ankara : The Department of Computer Engineering and The Graduate School of Engineering and Science of Bilkent Univesity, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 55-59.In real world, it is crucial to learn biomechanical strategies that prepare the body in kinematics and kinetics terms during the interception tasks, such as kicking, throwing and catching. Based on this, we presents a real-time physics-based approach that generate natural and physically plausible motions for a highly complex task- ball catching. We showed that ball catching behavior as many other complex tasks, can be achieved with the proper combination of rather simple motor skills, such as standing, walking, reaching. Since learned biomechanical strategies can increase the conscious in motor control, we concerned several issues that needs to be planned. Among them, we intensively focus on the concept of timing. The character learns some policies to know how and when to react by using reinforcement learning in order to use time accurately. We demonstrate the effectiveness of our method by presenting some of the catching animation results executed in different catching strategies.In each simulation, the balls were projected randomly, but within a interval of limits, in order to obtain different arrival flight time and height conditions.Çimen, GökçenM.S

Learning-based methods for planning and control of humanoid robots

Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3

Effects of Saltatory Rewards and Generalized Advantage Estimation on Reference-Based Deep Reinforcement Learning of Humanlike Motions

In the application of learning physics-based character skills, deep reinforcement learning (DRL) can lead to slow convergence and local optimum solutions during the training process of a reinforcement learning (RL) agent. With the presence of an environment with reward saltation, we can easily plan to magnify those saltatory rewards with the perspective of sample usage to increase the experience pool of an agent during this training process. In our work, we have proposed two modified algorithms. The first one is the addition of a parameter based reward optimization process to magnify the saltatory rewards and thus increasing an agent’s utilization of previous experiences. We have added this parameter based reward optimization with proximal policy optimization (PPO) algorithm. What’s more, the other proposed algorithm introduces generalized advantage estimation in estimating the advantage of the advantage actor critic (A2C) algorithm which resulted in faster convergence to the global optimal solutions of DRL. We have conducted all our experiments to measure their performances in a custom reinforcement learning environment built using a physics engine named PyBullet. In that custom environment, the RL agent has a humanoid body which learns humanlike motions, e.g., walk, run, spin, cartwheel, spinkick, and backflip, from imitating example reference motions using the RL algorithms. Our experiments have shown significant improvement in performance and convergence speed of DRL in this custom environment for learning humanlike motions using the modified versions of PPO and A2C if compared with their vanilla versions