FAKE Lab.

In this study, we develop a numerical method to represent turbulent flow in various 2D vector fields using the Monte Carlo method-based MLS (Moving Least Squares) from a density field and express it as a learning representation through a neural network. Conventional MLS performs high-order interpolation solely based on vector-based constraints, making it difficult to effectively reflect the characteristics of a density field, which limits its applicability in various fields. Additionally, equations of higher degree require significant computational effort, making them costly in terms of computation time and resources when applied to simulation tasks that require per-frame calculations. To address these issues, this study integrates the Monte Carlo method-based weighting into MLS to efficiently consider the characteristics of the density field in the input data and design an algorithm that represents it as various forms of vector fields. Furthermore, we extend the solver to express this approach as a learning representation through a neural network. To validate the applicability of our method, we conducted experiments extracting turbulent vector fields from various density fields. Since conventional MLS does not guarantee temporal continuity, directly applying it to simulations results in noise. To resolve this, our method generates turbulent flow by analyzing the angular variation between the generated velocity and the underlying fluid velocity, ensuring stable advection of the density. As a result, our method efficiently and accurately extracts turbulent flow from a density field and integrates it into an underlying fluid solver, enabling the practical use of high-order interpolation in physics-based simulations. Experimental results across various scenarios demonstrate that our method improves both computation time and quality compared to previous methods, yielding enhanced turbulent flow fields.

This paper proposes a 3D visual tool based on physically-based simulation to verify and analyze changes in the volume and length of hair after treatment with cosmetics or other chemicals. Unlike fur and hair simulations in virtual environments, real hair undergoes various chemical treatments not only to enhance hair quality but also to increase hair density. Such treatments are important as they lead to hairstyling, which significantly affects a person’s impression. We propose a novel visual tool that visualizes and analyzes changes in the length and volume of original strands before and after chemical treatment based on physically-based hair simulation. The results are applied to various scenarios, including back hair volume, root hair volume, side hair volume, crown hair volume, and hair sagging, to evaluate its effectiveness.

Simulating the cohesion, adhesion, stiffness, and exaggeration of curls of wet curly hair or fur, expressed through the saturation-hair interaction in physics-based simulations, remains a challenging problem. Wet hair or fur tends to clump and stiffen at the ends, a common phenomenon observed in wet hair or animal fur. Additionally, while wet hair should exhibit adhesion when in contact with solids, the uneven distribution of forces in wet curly hair, manifested as noise, complicates an accurate representation of adhesion. Research into detailed porous models for wet curly hair, driven by saturation-hair interaction, is not yet extensively explored. Previous methods have manually represented wet hair or used static hairstyles for wet curly hair and fur, maintaining shape but resulting in unnatural movement due to the lack of simulation. This paper proposes methods for representing wet curly hair features: 1) curl exaggeration using locally transformed helices, 2) deformation-based cohesion that remains stable in wet curly hair, 3) level-set-based adhesion for efficiently depicting the sticky and elongated forms of wet hair, 4) dynamic stiffness for improved simulation stability, and 5) collecting a detailed synthetic dataset of curly hairs and extending the solver to represent particle movements in wet strands through learning. Experiments in various scenes demonstrate that our proposed methods more realistically represent the saturation-hair interaction compared to previous wet curly hair simulations. Unlike previous methods in which saturation caused cohesion or curls to tangle, our method stably represents porous flow at the strand level. Additionally, we propose to extend the learning representation solver through both numerical simulation algorithms and AI-based approaches.

국방혁신 4.0의 도입과 함께, 대한민국은 첨단 기술을 군사 훈련에 적극적으로 통합하고 있다. 현재 대한민국 군에서 사용되는 사격 훈련 방식은 프로젝터 기반 가상현실(VR) 시스템 또는 비시스루 비디오(Non Video See-Through) 방식의 HMD를 활용하는데, 이 방식은 더 많은 가상 콘텐츠 자원 (아바타 및 환경)이 필요하며 그에 따른 시스템 지원 사항(렌더링 등)이 필요하다.. 이러한 문제를 해결하기 위해 본 연구에서는 증강현실(AR) 기반 사격 훈련 시스템을 제안하며, Hololens 2와 VIVE 트래커를 활용하여 실제 총기의 위치와 신체 움직임을 정밀하게 추적하는 방법을 구현하였다. 추가적으로 Proximal Policy Optimization(PPO) 알고리즘 기반의 강화학습 AI 어시스턴트를 도입하여, 사격의 정확도와 정밀도를 분석하고 실시간 피드백을 제공하여 사용자의 자세를 개선하도록 설계하였다. 또한, TCP 및 UDP 기반 소켓 통신 방식을 채택하여 평균 2.30ms의 낮은 지연시간을 달성하며 실시간 훈련이 가능함을 검증하였다.

We propose a novel method conceptualized from the properties of physics where in particular the shape of a flame is determined by temperature that enables a control mechanism for the intuitive shaping of a flame. We focused on a trade-off issue from computer graphics whereby the turbulent flow that expresses the characteristics of the flame has a tendency to shift continuously, whereas the velocity constraints that contain a fluid within a target shape have a tendency to force movement in a particular direction. Trade-off made it difficult for animation designers to maintain a flame within the intended target shape. This paper resolves the issue by enabling the flame to be controlled without any velocity constraints by using the following two techniques: First, we model the temperature and force of the explosion generated by the combustion of explosive gaseous fuel and apply it to certain regions. Second, we expand the space of the interface between the fuel and the burned products, classifying that space into four regions and controlling the target shape of the flame by delicate adjustments to the temperature in each region. Experiments show that the flame maintains the appearance of dynamic movement while preserving the detailed 3D shapes specified by the scene designers.

미시적인 관점에서 물표면 주위에 위치한 물 입자와 공기 입자는 끊임없이 서로 상호작용을 한다. 이러한 상호작용은 대량의 작은 물 입자들이 엷게 흩날리는 상황이 표현되는 폭포나 바다에서 명확하게 나타난다. 즉, 엷게 퍼진 작은 물 입자들로 인해 물과 공기사이의 표면경계가 불분명해지며 이 부분에서 공기와 물 입자간의 상호작용으로 인해 급격한 와류현상이 나타나게 된다. 그러나 기존 입자 기반 물 시뮬레이션에서는 유동에 의해 나타나는 자기와류(self-trubulent)에만 집중하였고, 자유표면 근처에서 공기에 의해 표현되는 부차적인 와류 현상에 대해서는 고려하지 못했다. 유체표면의 움직임에 집중된 모델링으로 인해 대량의 작은 물 입자들이 엷게 흩날리는 장면을 사실적으로 연출하기에는 한계가 있다. 우리는 1)물 표면에서 공기의 역할을 담당하는 공기 입자 층을 적응적으로 생성하고, 2)물과 공기를 서로 다른 상 (phase)으로 모델링하여 자유표면 근처에서 발생하는 와류를 사실적으로 표현하는 기법을 제안한다. 결과적으로, 우리는 공기에 의해 표현되는 와류를 입자기반 프레임워크에서 효율적으로 다루어 계산속도 및 결과 측면에서 기존 기법보다 개선된 결과를 얻었다.