Pose-centric motion synthesis through adaptive instance normalization

In pose-centric motion synthesis, existing methods often depend heavily on architecture-specific mechanisms to comprehend temporal dependencies. This paper addresses this challenge by introducing the use of adaptive instance normalization layers to capture temporal coherence within pose-centric motion synthesis. We demonstrate the effectiveness of our contribution through state-of-the-art performance in terms of Fréchet Inception Distance (FID) and comparable diversity scores. Evaluations conducted on the CMU MoCap and the HumanAct12 datasets showcase our method‘s ability to generate plausible and high-quality motion sequences, underscoring its potential for diverse applications in motion synthesis.