Generating Audiovisual Synergy Fluid Animation for Highly Immersive VR Experience
Na Jiang, Xiangcheng Zhai, Yuxuan Qiu, Xiaohui Tan, Aimin Hao, Yang Gao
Abstract
Generative content is increasingly applied in VR to provide immersive experiences, yet maintaining high generation quality remains challenging for audiovisual effects. Particularly in dynamic fluid phenomena, achieving realism and presence requires adherence to physical laws. To accomplish this objective, this work proposes an audiovisual synergy fluid animation generation framework, which enhances immersion by improving motion texture fidelity and audiovisual consistency. It comprises Detail-Enhanced Texture generator (DET) and Physics-Guided Audio generator (PGA). DET integrates Global-Local Physics guidance (GLP) and Temporal Texture Modeling (TTM) to produce video textures, explicitly optimizing dynamic details by leveraging local motion cues and assigned cumulative differences. PGA incorporates Visual Semantic Augmenter (VSA) and Rhythm Semantic Adapter (RSA) to synchronize audio by fusing static visual semantics with dynamic motion semantics to improve temporal coherence. By integrating DET and PGA, this framework strengthens audiovisual immersion in VR natural dynamic scenes from both visual and auditory perspectives. Quantitative and qualitative evaluations demonstrate that our approach surpasses most existing methods in terms of texture realism and audiovisual synchronization, offering new insights for advancing immersive experiences in dynamic VR phenomena.
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