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We present a practical data-driven method for automatically synthesizing plausible soundtracks for physics-based cloth animations running at graphics rates. Given a cloth animation, we analyze the deformations and use motion events to drive crumpling and friction sound models estimated from cloth measurements. We synthesize a low-quality sound signal, which is then used as a target signal for a concatenative sound synthesis (CSS) process. CSS selects a sequence of microsound units, very short segments, from a database of recorded cloth sounds, which best match the synthesized target sound in a low-dimensional feature-space after applying a hand-tuned warping function. The selected microsound units are concatenated together to produce the final cloth sound with minimal filtering. Our approach avoids expensive physics-based synthesis of cloth sound, instead relying on cloth recordings and our motion-driven CSS approach for realism. We demonstrate its effectiveness on a variety of cloth animations involving various materials and character motions, including first-person virtual clothing with binaural sound.
From cloth simulations sliding velocities and crumpling events are extracted and used to synthesize sounds. The parametric noise model for sliding uses samples from recording of a sliding cylinder. For crumpling, each significant crumpling event from simulation is assigned to a recorded crumpling sound with similar energy. For better realism, a manual coupling is made between selected parts of the target signal and signal fragments from a database. A warping function completes the mapping between target and database clips.
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