Learning Representation of Secondary Effects for Fire-Flake Animation

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2021.3054061