Abstract
We introduce the first method to automatically generate 3D mesh sequences from text, inspired by the challenging problem of Sign Language Production (SLP). The approach only requires simple 2D annotations for training, which can be automatically extracted from video. Rather than incorporating high-definition or motion capture data, we propose back-translation as a powerful paradigm for supervision: By first addressing the arguably simpler problem of translating 2D pose sequences to text, we can leverage this to drive a transformer-based architecture to translate text to 2D poses. These are then used to drive a 3D mesh generator. Our mesh generator Pose2Mesh uses temporal information, to enforce temporal coherence and significantly reduce processing time. The approach is evaluated by generating 2D pose, and 3D mesh sequences in DGS (German Sign Language) from German language sentences. An extensive analysis of the approach and its sub-networks is conducted, reporting BLEU and ROUGE scores, as well as Mean 2D Joint Distance. Our proposed Text2Pose model outperforms the current state-of-the-art in SLP, and we establish the first benchmark for the complex task of text-to-3D-mesh-sequence generation with our Text2Mesh model.