Visual simulation of fire-flakes synchronized with flame

Kim, Tae-Hyeong; Hong, Eunki; Im, Jaeho; Yang, Dae Sik; Kim, Youngbin; Kim, Chang-Hun

doi:10.1007/s00371-017-1374-9

Cited by 8 publications

(12 citation statements)

References 15 publications

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“…In water simulation, foam, bubbles, and splash particles are typical [23], while fire simulation is a typical secondary effect. Recently, Kim et al proposed the fire flake simulation technique for the first time in CG field [5]. This technique also calculates the motion of fire flake particles based on the underlaying simulation.…”

Section: Discussionmentioning

confidence: 99%

“…Kim et al proposed a method of containing flame in a target shape by controlling the temperature [3]. In the field of fire simulation, a technique has been proposed to express the motion of fire-flake as well as flame according to the temperature field and velocity field of fluid [5]. The simulated fire-flake motion in this technique is not sufficient to represent realistic fire-flake effects because it is a random walk based, with somewhat noisy motion unlike flame.…”

Section: Related Workmentioning

confidence: 99%

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Fire Sprite Animation Using Fire-Flake Texture and Artificial Motion Blur

Kim

jung

2019

IEEE Access

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In this paper, we propose a sprite animation synthesis technique that can efficiently represent the fire-flake effects seen in the natural phenomenon of fire. The proposed method uses the actual fire video or animated fire video as inputs and performs the following steps: 1) Extraction of feature vectors that can predict the direction of flame from image, 2) calculation of artificial buoyancy field, 3) creation and advection of fire-flake texture, 4) calculation of artificial motion blur using buoyancy flow, and 5) high quality composition. First, we detect the edges from the image and calculate the feature vectors needed to calculate the artificial buoyancy field. The computed 2D feature vectors are integrated into the Navier-Stokes equation and used to calculate the buoyancy field, which generates and advects anisotropic fire-flake textures. Finally, we apply artificial motion blur according to buoyancy direction to improve composition result of sprite animation. As a result, this method is based on image synthesis, which is faster than the existing 3D simulation-based approach. Experimental results show that high quality results can be easily and reliably obtained. In addition, since the final result is a sprite animation format, it can be easily used in existing game engines. INDEX TERMS Sprite animation, fire effects, anisotropic fire-flake texture, artificial buoyancy field. FIGURE 1. Various fire effects in animation and game (a: animation 'ZETMAN', b: mobile game 'Raising Fire Magician'.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fire Sprite Animation Using Fire-Flake Texture and Artificial Motion Blur

Kim

jung

2019

IEEE Access

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“…Chang et al introduced a Compute Unified Device Architecture (CUDA)‐based framework to generate dynamic fireworks with high efficiency and frame rates . Kim et al introduced a sample‐based control method that enables the formation of a target shape by assembling fire‐flakes . However, these methods do not tackle fireworks simulations for 3D models with complex structures.…”

Section: Related Workmentioning

confidence: 99%

“…15 Kim et al introduced a sample-based control method that enables the formation of a target shape by assembling fire-flakes. 16 However, these methods do not tackle fireworks simulations for 3D models with complex structures. In particular, existing point sampling algorithms in these methods fail to approximate sharp outlines and can only associate one or a limited number of colors with the simulation, which largely undermine the vividness and realism of fireworks effects.…”

Section: Firework Simulationsmentioning

confidence: 99%

Sketch‐based shape‐constrained fireworks simulation in head‐mounted virtual reality

Cui

Cai

Tang

et al. 2020

Computer Animation & Virtual

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In this paper we present a novel shape‐constrained fireworks simulation method with rich textures in an HMD (Helmet Mounted Display) virtual environment using sketched feature lines as input. Our approach first retrieves an object from a three‐dimensional (3D) model database using a sketch‐based 3D shape retrieval algorithm. Then, in order to approximate models with complex structures, we introduce a novel point sampling algorithm based on Gaussian curvatures, which stores not only the positions of the selected vertices but also the texture (UV) coordinates information for texture display. In addition, we introduce a multilevel explosion process so that the fireworks can dynamically form specific, visually pleasing shapes. Through our experiments, we demonstrate that our approach can produce better results than state‐of‐the‐art approaches.

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“…Realizing an accurate simulation of fire flakes is challenging; however, their physical attributes can be inferred. Kim et al [1] proposed a method to generate fire flakes that fit naturally in the surroundings. Abrupt changes in physical components are analyzed to generate fire flakes in appropriate locations, and the velocity field is analyzed to give the fire flakes a natural-looking velocity.…”

Section: Introductionmentioning

confidence: 99%

Learning Representation of Secondary Effects for Fire-Flake Animation

Choi

Kim³

et al. 2021

IEEE Access

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This paper proposes a new data-driven neural network-based fire-flake simulation model. Our model trains a neural network using precomputed fire simulation data. The trained neural network model generates fire flakes in appropriate locations and infers their velocity to make them appear natural to their surroundings. The neural network model consists of a fire-flake generator and a velocity modifier. The fireflake generator uses the velocity, temperature, and density fields of the precomputed fire simulation as inputs to determine the locations at which natural fire flakes would be generated. The velocity modifier takes the velocity field of the precomputed fire simulation as input and infers the velocity of the generated fire flakes so that they appear natural relative to the flame motions and surroundings. Our method adopts a neural network to efficiently improve the fire-flake simulation, enhancing the performance while maintaining the visual quality. Our model is approximately three times faster than the traditional fire-flake model. In particular, our model is 30 times faster in the velocity modification step. Our method is also easier to implement than the existing physically based fire-flake simulation method and can reduce the time spent by artists and developers on their applications.

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Visual simulation of fire-flakes synchronized with flame

Cited by 8 publications

References 15 publications

Fire Sprite Animation Using Fire-Flake Texture and Artificial Motion Blur

Fire Sprite Animation Using Fire-Flake Texture and Artificial Motion Blur

Sketch‐based shape‐constrained fireworks simulation in head‐mounted virtual reality

Learning Representation of Secondary Effects for Fire-Flake Animation

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