  Affordance learning considers the interaction opportunities for an actor in
the scene and thus has wide application in scene understanding and intelligent
robotics. In this paper, we focus on contextual affordance learning, i.e.,
using affordance as context to generate a reasonable human pose in a scene.
Existing scene-aware human pose generation methods could be divided into two
categories depending on whether using pose templates. Our proposed method
belongs to the template-based category, which benefits from the representative
pose templates. Moreover, inspired by recent transformer-based methods, we
associate each query embedding with a pose template, and use the interaction
between query embeddings and scene feature map to effectively predict the scale
and offsets for each pose template. In addition, we employ knowledge
distillation to facilitate the offset learning given the predicted scale.
Comprehensive experiments on Sitcom dataset demonstrate the effectiveness of
our method.
