Our　work　presents　a　novel　spectrum-inspired　learning-based　approach　for　generating　clothing　deformations　with　dynamic　effects　and　personalized　details.　Existing　methods　in　the　field　of　clothing　animation　are　limited　to　either　static　behavior　or　specific　network　models　for　individual　garments,　which　hinders　their　applicability　in　real-world　scenarios　where　diverse　animated　garments　are　required.　Our　proposed　method　overcomes　these　limitations　by　providing　a　unified　framework　that　predicts　dynamic　behavior　for　different　garments　with　arbitrary　topology　and　looseness,　resulting　in　versatile　and　realistic　deformations.　First,　we　observe　that　the　problem　of　bias　towards　low　frequency　always　hampers　supervised　learning　and　leads　to　overly　smooth　deformations.　To　address　this　issue,　we　introduce　a　frequency-control　strategy　from　a　spectral　perspective　that　enhances　the　generation　of　high-frequency　details　of　the　deformation.　In　addition,　to　make　the　network　highly　generalizable　and　able　to　learn　various　clothing　deformations　effectively,　we　propose　a　spectral　descriptor　to　achieve　a　generalized　description　of　the　global　shape　information.　Building　on　the　above　strategies,　we　develop　a　dynamic　clothing　deformation　estimator　that　integrates　graph　attention　mechanisms　with　long　short-term　memory.　The　estimator　takes　as　input　expressive　features　from　garments　and　human　bodies,　allowing　it　to　automatically　output　continuous　deformations　for　diverse　clothing　types,　independent　of　mesh　topology　or　vertex　count.　Finally,　we　present　a　neural　collision　handling　method　to　further　enhance　the　realism　of　garments.　Our　experimental　results　demonstrate　the　effectiveness　of　our　approach　on　a　variety　of　free-swinging　garments　and　its　superiority　over　state-of-the-art　methods.