How　to　generate　well　quality　faces　objects　of　automated　processes　has　always　been　the　focus　on　researchers.　Recently,　due　to　the　deep　generative　networks　have　achieved　impressive　successes　in　data　generative　fields,　researchers　have　tried　to　introduce　deep　learning　into　the　3d　objects　generate　field,　such　as　text2scene,　slice-based　object　generate.　However,　the　generative　ability　in　3D　object　is　limited　by　the　size　of　the　feature　space,　because　of　computational　space　limitations　on　hardware.　In　this　paper,　we　address　the　problem　by　reducing　amount　of　calculated　on　process　of　learning,　and　thus　generative　newly　different　objects.　The　problem　is　intractable,　since　first　the　limiting　of　compute　space　is　so　hard　that　object　can＇t　be　process　in　deep　network　due　to　the　process　need　to　compute　many　matrix　multiplications.　To　resolve　the　problem,　we　propose　a　sparse　representation　based　method　of　generating　well-quality　faces　object.　Our　method　consists　of　two　parts:　sparse　reconstruction　and　object　generative.　First,　we　verified　the　possibility　of　using　sparse　representations　of　3D　data　by　reconstructing　3D　object.　Second,　we　design　a　network　architecture　of　deep　adversarial　network　of　generating　new　sparse　representation　and　combined　with　the　previous　reconstruction　method　of　generating　new　face　object.　Experiments　show　that　our　method　has　the　ability　to　generate　very　different　and　well　quality　faces　objects　that　contain　tens　of　thousands　of　points　and　meshes.　Our　findings　show　that　sparse　representation　can　be　used　in　3D　object　reconstruction　and　generate　via　deep　generative　adversarial　model.