  Video transformers have become the de facto standard for human action
recognition, yet their exclusive reliance on the RGB modality still limits
their adoption in certain domains. One such domain is Activities of Daily
Living (ADL), where RGB alone is not sufficient to distinguish between visually
similar actions, or actions observed from multiple viewpoints. To facilitate
the adoption of video transformers for ADL, we hypothesize that the
augmentation of RGB with human pose information, known for its sensitivity to
fine-grained motion and multiple viewpoints, is essential. Consequently, we
introduce the first Pose Induced Video Transformer: PI-ViT (or $\pi$-ViT), a
novel approach that augments the RGB representations learned by video
transformers with 2D and 3D pose information. The key elements of $\pi$-ViT are
two plug-in modules, 2D Skeleton Induction Module and 3D Skeleton Induction
Module, that are responsible for inducing 2D and 3D pose information into the
RGB representations. These modules operate by performing pose-aware auxiliary
tasks, a design choice that allows $\pi$-ViT to discard the modules during
inference. Notably, $\pi$-ViT achieves the state-of-the-art performance on
three prominent ADL datasets, encompassing both real-world and large-scale
RGB-D datasets, without requiring poses or additional computational overhead at
inference.
