Learning Representation of Secondary Effects for Fire-Flake Animation

Abstract

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.

The Metaverse: Applications, Concerns, Technical Challenges, Future Directions and Recommendations

“…Exploring the Intersection of AI and the Metaverse: As AI technology advances, it is expected to play an increasingly important role in the Metaverse. Future research should explore the intersection of AI and the Metaverse, and investigate how AI can be used to enhance user experiences, create more realistic virtual environments, and support new applications and use cases [36].…”

Refinement of Projection Map Based on Artificial Neural Networks to Represent Noise-Reduced Foam Effects

“…물리 기반 유체 시뮬레이션은 물 [1,2], 불 [3,4,5], 연기 [6,7,8], 불똥 [9,10,11], 거품 [12,13], 버블 [14,15], 미스트 [16,17 [22,23].…”

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks

“…Physics-based fluid simulation has been used to realize various visual special effects to simulate water [ 1 , 2 ], fire [ 3 – 5 ], smoke [ 6 – 8 ], fire-flake [ 9 – 11 ], foam [ 12 , 13 ], bubble [ 14 , 15 ], and mist (or spray) [ 16 , 17 ]. When expressing water, the associated secondary effects such as foam, bubble, and splash are caused by oscillating movements, and various approaches have been proposed to efficiently model these characteristics [ 18 , 19 ].…”