Transformer-based Neuro-Animator for Qualitative Simulation of Soft Body Movement

[Submitted on 10 Aug 2024]

View a PDF of the paper titled Transformer-based Neuro-Animator for Qualitative Simulation of Soft Body Movement, by Somnuk Phon-Amnuaisuk

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Abstract:The human mind effortlessly simulates the movements of objects governed by the laws of physics, such as a fluttering, or a waving flag under wind force, without understanding the underlying physics. This suggests that human cognition can predict the unfolding of physical events using an intuitive prediction process. This process might result from memory recall, yielding a qualitatively believable mental image, though it may not be exactly according to real-world physics. Drawing inspiration from the intriguing human ability to qualitatively visualize and describe dynamic events from past experiences without explicitly engaging in mathematical computations, this paper investigates the application of recent transformer architectures as a neuro-animator model. The visual transformer model is trained to predict flag motions at the emph{t+1} time step, given information of previous motions from emph{t-n} $cdots$ emph{t} time steps. The results show that the visual transformer-based architecture successfully learns temporal embedding of flag motions and produces reasonable quality simulations of flag waving under different wind forces.