Estimating　3D　hand　pose　from　a　single　RGB　image　is　a　challenging　task　because　of　its　ill-posed　nature　(i.e.,　depth　ambiguity).　Recently,　various　generative　approaches　have　been　proposed　to　predict　the　3D　joints　of　an　RGB　hand　image　by　learning　a　unified　latent　space　between　two　modalities　(i.e.,　RGB　image　and　3D　joints).　However,　projecting　multi-modal　data　(i.e.,　RGB　images　and　3D　joints)　into　a　unified　latent　space　is　difficult　as　the　modality-specific　features　usually　interfere　the　learning　of　the　optimal　latent　space.　Hence　in　this　paper,　we　propose　to　disentangle　the　latent　space　into　two　sub-latent　spaces:　modalityspecific　latent　space　and　pose-specific　latent　space　for　3D　hand　pose　estimation.　Our　proposed　method,　namely　Dis-entangled　Cross-Modal　Latent　Space　(DCMLS),　consists　of　two　variational　autoencoder　networks　and　auxiliary　components　which　connect　the　two　VAEs　to　align　underlying　hand　poses　and　transfer　modality-specific　context　from　RGB　to　3D.　For　the　hand　pose　latent　space,　we　align　it　with　the　two　modalities　by　using　a　cross-modal　discriminator　with　an　adversarial　learning　strategy.　For　the　context　latent　space,　we　learn　a　context　translator　to　gain　access　to　the　cross-modal　context.　Experimental　results　on　two　widely　used　public　benchmark　datasets　RHD　and　STB　demonstrate　that　our　proposed　DCMLS　method　is　able　to　clearly　out-perform　the　state-of-the-art　ones　on　single　image　based　3D　hand　pose　estimation.