3D　human　pose　and　shape　estimation　is　often　impacted　by　distribution　bias　in　real-world　scenarios　due　to　factors　such　as　bone　length,　camera　parameters,　background,　and　occlusion.　To　address　this　issue,　we　propose　the　Confidence　Sharing　Adaptation　(CSA)　algorithm,　which　corrects　model　bias　using　unlabeled　images　from　the　test　domain　before　testing.　However,　the　lack　of　annotation　constraints　in　the　adaptive　training　process　poses　a　significant　challenge,　making　it　susceptible　to　model　collapse.　CSA　utilizes　a　decoupled　dual-branch　learning　framework　to　provide　pseudo-labels　and　remove　noise　samples　based　on　the　confidence　scores　of　the　inference　results.　By　sharing　the　most　confident　prior　knowledge　between　the　dual-branch　networks,　CSA　effectively　mitigates　distribution　bias.　CSA　is　also　remarkably　adaptable　to　severely　occluded　scenes,　thanks　to　two　auxiliary　techniques:　a　self-attentive　parametric　regressor　that　ensures　robustness　to　occlusion　of　local　body　parts　and　a　rendered　surface　texture　loss　that　regulates　the　relationship　between　occlusion　of　human　joint　positions.　Evaluation　results　show　that　CSA　successfully　adapts　to　scenarios　beyond　the　training　domain　and　achieves　state-of-the-art　performance　on　both　occlusion-specific　and　general　benchmarks.　Code　and　pre-trained　models　are　available　for　research　at　https://github.com/bodymapper/csa.git　©　2024　Elsevier　Inc.