  Transformers have recently been shown to generate high quality images from
text input. However, the existing method of pose conditioning using skeleton
image tokens is computationally inefficient and generate low quality images.
Therefore we propose a new method; Keypoint Pose Encoding (KPE); KPE is 10
times more memory efficient and over 73% faster at generating high quality
images from text input conditioned on the pose. The pose constraint improves
the image quality and reduces errors on body extremities such as arms and legs.
The additional benefits include invariance to changes in the target image
domain and image resolution, making it easily scalable to higher resolution
images. We demonstrate the versatility of KPE by generating photorealistic
multiperson images derived from the DeepFashion dataset. We also introduce a
evaluation method People Count Error (PCE) that is effective in detecting error
in generated human images.
