Generating realistic 3D human motion has been a fundamental goal of the game/animation industry. This work presents a novel transition generation technique that can bridge the actions of people in the foreground by generating 3D poses and shapes in-between photos, allowing 3D animators/novice users to easily create/edit 3D motions. To achieve this, we propose an adaptive motion network (ADAM-Net) that effectively learns human motion from masked action sequences to generate kinematically compliant 3D poses and shapes in-between given temporally-sparse photos. Three core learning designs underpin ADAM-Net. First, we introduce a random masking process that randomly masks images from an action sequence and fills masked regions in latent space by interpolation of unmasked images to simulate various transitions under given temporally-sparse photos. Second, we propose a long-range adaptive motion (L-ADAM) attention module that leverages visual cues observed from human motion to adaptively recalibrate the range that needs attention in a sequence, along with a multi-head cross-attention. Third, we develop a short-range adaptive motion (S-ADAM) attention module that weightedly selects and integrates adjacent feature representations at different levels to strengthen temporal correlation. By coupling these designs, the results demonstrate that ADAM-Net excels not only in generating 3D poses and shapes in-between photos, but also in classic 3D human pose and shape estimation.