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We present a simple unsupervised method for learning an encoder mapping short3D pose sequences into embedding vectors suitable for sequence-to-sequencealignment by dynamic time warping. Training samples consist of temporal windowsof frames containing 3D body points such as mocap markers or skeleton joints. Alight-weight, 3-layer encoder is trained using a contrastive loss function thatencourages embedding vectors of augmented sample pairs to have cosinesimilarity 1, and similarity 0 with all other samples in a minibatch. Whenmultiple scripted training sequences are available, temporal alignmentsinferred from an initial round of training are harvested to extract additional,cross-performance match pairs for a second phase of training to refine theencoder. In addition to being simple, the proposed method is fast to train,making it easy to adapt to new data using different marker sets or skeletaljoint layouts. Experimental results illustrate ease of use, transferability,and utility of the learned embeddings for comparing and analyzing humanbehavior sequences.

A Light-Weight Contrastive Approach for Aligning Human Pose Sequences