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Humans exhibit complex motions that vary depending on the task that they areperforming, the interactions they engage in, as well as subject-specificpreferences. Therefore, forecasting future poses based on the history of theprevious motions is a challenging task. This paper presents an innovativeauxiliary-memory-powered deep neural network framework for the improvedmodelling of historical knowledge. Specifically, we disentanglesubject-specific, task-specific, and other auxiliary information from theobserved pose sequences and utilise these factorised features to query thememory. A novel Multi-Head knowledge retrieval scheme leverages thesefactorised feature embeddings to perform multiple querying operations over thehistorical observations captured within the auxiliary memory. Moreover, ourproposed dynamic masking strategy makes this feature disentanglement processdynamic. Two novel loss functions are introduced to encourage diversity withinthe auxiliary memory while ensuring the stability of the memory contents, suchthat it can locate and store salient information that can aid the long-termprediction of future motion, irrespective of data imbalances or the diversityof the input data distribution. With extensive experiments conducted on twopublic benchmarks, Human3.6M and CMU-Mocap, we demonstrate that these designchoices collectively allow the proposed approach to outperform the currentstate-of-the-art methods by significant margins: $>$ 17\% on the Human3.6Mdataset and $>$ 9\% on the CMU-Mocap dataset.

Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction