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It takes less than half a second for a person to fall [8]. Capturing the essence of a fall from video or motion capture is difficult. More generally, generating realistic 3D human body motions from motion capture (MoCap) data is a significant challenge with potential applications in animation, gaming, and robotics. Current motion datasets contain single-labeled activities, which lack fine-grained control over the motion, particularly for actions as sparse, dynamic, and complex as falling. This work introduces a novel human falling dataset and a learned multi-branch, Attribute-Conditioned Variational Autoencoder model to generate novel falls. Our unique dataset introduces a new ontology of the motion into three phases: Impact, Glitch, and Fall. Each branch of the model learns each phase separately and the fusion layer learns to fuse the latent space together. Furthermore, we present data augmentation techniques and an inter-phase smoothness loss for natural plausible motion generation. We successfully generated high-quality images, validating the efficacy of our model in producing high-fidelity, attribute-conditioned human movements.