• Journal of the Korea Computer Graphics Society
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• v.26 no.4
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• pp.1-8
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• 2020

Motion synthesis using physics-based controllers can generate a character animation that interacts naturally with the given environment and other characters. Recently, various methods using deep neural networks have improved the quality of motions generated by physics-based controllers. In this paper, we present a control policy learned by deep reinforcement learning (DRL) that enables Luxo, the mascot character of Pixar animation studio, to run towards a random goal location while imitating a reference motion and maintaining its balance. Instead of directly training our DRL network to make Luxo reach a goal location, we use a reference motion that is generated to keep Luxo animation's jumping style. The reference motion is generated by linearly interpolating predetermined poses, which are defined with Luxo character's each joint angle. By applying our method, we could confirm a better Luxo policy compared to the one without any reference motions.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.133-142
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• 2019

In physics-based character animation, trajectory optimization has been widely adopted for automatic motion synthesis, through the prediction of an optimal sequence of future states of the character based on its system dynamics model. In general, the system dynamics model is neither in a closed form nor differentiable when it handles the contact dynamics between a character and the environment with rigid body collisions. Employing smoothed contact dynamics, researchers have suggested efficient trajectory optimization techniques based on numerical differentiation of the resulting system dynamics. However, the numerical derivative of the system dynamics model could be inaccurate unlike its analytical counterpart, which may affect the stability of trajectory optimization. In this paper, we propose a novel method to derive the closed-form derivative for the system dynamics by properly approximating the contact model. Based on the resulting derivatives of the system dynamics model, we also present a model predictive control (MPC)-based motion synthesis framework to robustly control the motion of a biped character according to on-line user input without any example motion data.

• Journal of the Korea Computer Graphics Society
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• v.21 no.5
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• pp.29-36
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• 2015

Human fingers are essential parts of the body that perform complex and detailed motion. Expression of natural finger motion is one of the most important issues in character animation research. Especially, keyboard typing animation is hard to create through the existing animation pipeline because the keyboard typing typically requires a high level of dexterous motion that involves the movement of various joints in a natural way. In this paper, we suggest a method for the generation of realistic keyboard typing motion based on physics simulation. To generate typing motion properly using physics-based simulation, the hand and the keyboard models should be positioned in an allowed range of simulation space, and the typing has to occur at a precise key location according to the input signal. Based on the observation, we incorporate natural tendency that accompanies actual keyboard typing. For example, we found out that the positions of the hands and fingers always assume the default pose, and the idle fingers tend to minimize their motion. We handle these various constraints in one solver to achieve the results of real-time natural keyboard typing simulation. These results can be employed in various animation and virtual reality applications.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.105-113
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• 2017

This paper presents a novel on-line trajectory optimization framework based on automatic time warping, which performs the time warping of a reference motion while optimizing character motion control. Unlike existing physics-based character animation methods where sampling times for a reference motion are uniform or fixed during optimization in general, our method considers the change of sampling times on top of the dynamics of character motion in the same optimization, which allows the character to effectively respond to external pushes with optimal time warping. In order to do so, we formulate an optimal control problem which takes into account both the full-body dynamics and the change of sampling time for a reference motion, and present a model predictive control framework that produces an optimal control policy for character motion and sampling time by repeatedly solving the problem for a fixed-span time window while shifting it along the time axis. Our experimental results show the robustness of our framework to external perturbations and the effectiveness on rhythmic motion synthesis in accordance with a given piece of background music.

• Journal of the Korea Computer Graphics Society
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• v.11 no.4
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• pp.39-44
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• 2005

인터랙티브 컴퓨터 게임과 컴퓨터 애니메이션에서, 유관절체의 움직임을 직관적으로 제어하도록 하는 것은 어려운 문제로 인식되고 있다. 이런 분야에서는 대부분 움직임의 대상이 되는 캐릭터가 많은 관절로 연결되어 있는데, 이때 각 관절을 사용자의 의도대로 쉽게 조종할 수 있도록 해주는 인터페이스를 디자인하기가 어렵기 때문이다. 본 논문에서는 자유도(DOF)가 높은 캐릭터의 움직임을 제어하기 위해 오랫동안 인형극에서 사용되고 있는 마리오넷 조종 기법[5]을 응용한 마리오넷 시스템을 제안하고자 한다. 우리는 가상 마리오넷 시스템을 물리기반 모델링과 햅틱 인터페이스를 기반으로 구현하였고, 이 시스템을 통해 높은 자유도를 가지는 유관절체 캐릭터의 복잡한 움직임을 쉽게 생성해낼 수 있었다. 그리고 사용자에게 햅틱 포스 피드백을 줌으로써 더욱 정교한 마리오넷을 조작이 가능하도록 하였다. 이 시스템을 일반적인 유관절체에 적용한다면 다양한 움직임을 쉽고 빠르게 생성할 수 있을 것이다.

• Journal of the Korea Computer Graphics Society
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• v.29 no.4
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• pp.7-16
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• 2023

For decades, creating a desired locomotive motion in a goal-oriented manner has been a challenge in character animation. Data-driven methods using generative models have demonstrated efficient ways of predicting long sequences of motions without the need for explicit conditioning. While these methods produce high-quality long-term motions, they can be limited when it comes to synthesizing motion for challenging novel scenarios, such as punching a random target. A state-of-the-art solution to overcome this limitation is by using a GAN Discriminator to imitate motion data clips and incorporating reinforcement learning to compose goal-oriented motions. In this paper, our research aims to create characters performing combat sports such as boxing, using a novel reward design in conjunction with existing GAN-based approaches. We experimentally demonstrate that both the Adversarial Motion Prior [3] and Adversarial Skill Embeddings [4] methods are capable of generating viable motions for a character punching a random target, even in the absence of mocap data that specifically captures the transition between punching and locomotion. Also, with a single learned policy, multiple task controllers can be constructed through the TimeChamber framework.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.420-425
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• 2008

The technique for synthesizing reactive motion in real-time is important in many applications such as computer games and virtual reality. This paper presents a dynamic motion control technique for creating reactive motions in a physically based character animation system. The leg to move in the next step is chosen using the direction of external disturbance forces and states of human figures and then is lifted though joint PD control. We decide the target position of the foot to balance the body without leg cross. Finally, control mechanism is used to generate reactive motion. The advantage of our method is that it is possible to generate reactive animations without example motions.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.616-618
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• 2001

천의 변형은 가상 현실이나 게임 제작 분야에서 현실감을 증가시키는 측면에서 필수적인 요소들 중의 하나이다. 반면, 옷을 착용하거나 깃발을 매단 캐릭터가 돌발적이면서 급격한 움직임을 나타낼 때에는, 이에 따른 천의 움직임을 원하는 시간내에 자연스럽게 생성하기가 쉽지 않다. 본 논문에서는 천의 사실적인 변형에 있어 필수 요소인 비선형성을 고려하면서도, 돌발적이고 빈번하게 작용하는 외부 힘에 대하여 안정적이고 빠른 위치 기반의 근사 방법을 제안하고자 한다. 또한, 이에 따른 사실성을 유지하기 위하여 스프링의 비선형적인 성분을 기하학적으로 처리하는 방법을 제안한다. 이 방법은 기하학적 관계에 물리적 속성을 반영하여 해결함으로써 시각적으로 받아들일 수 있는(visibly-plausible) 천의 자연스러운 움직임을 생성할 수 있다. 본 논문의 결과로 자동차의 급격한 움직임에도 안정적이고, 자동차가 달리는 방향이나 노면 등의 외부 환경의 변화에도 사실적인 천의 애니메이션을 생성할 수 있다.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.367-375
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• 2002

Inverse kinematics is a very useful method for control]ing the posture of an articulated body. In most inverse kinematics processes, the major matter of concern is not the posture of an articulated body itself but the position and direction of the end effector. In some applications such as 3D character animations, however, it is more important to generate an overall natural posture for the character rather than place the end effector in the exact position. Indeed, when an animator wants to modify the posture of a human-like 3D character with many physical constraints, he has to undergo considerable trial-and-error to generate a realistic posture for the character. In this paper, the Inductive Inverse Kinematics(IIK) algorithm using a Uniform Posture Map(UPM) is proposed to control the posture of a human-like 3D character. The proposed algorithm quantizes human behaviors without distortion to generate a UPM, and then generates a natural posture by searching the UPM. If necessary, the resulting posture could be compensated with a traditional Cyclic Coordinate Descent (CCD). The proposed method could be applied to produce 3D-character animations based on the key frame method, 3D games and virtual reality.