Learning Flow Fields in Attention for Controllable Person Image
  Generation
Authors: name: Zijian Zhou, name: Shikun Liu, name: Xiao Han, name: Haozhe Liu, name: Kam Woh Ng, name: Tian Xie, name: Yuren Cong, name: Hang Li, name: Mengmeng Xu, name: Juan-Manuel Pérez-Rúa, name: Aditya Patel, name: Tao Xiang, name: Miaojing Shi, name: Sen He

Controllable person image generation aims to generate a person image
conditioned on reference images, allowing precise control over the person's
appearance or pose. However, prior methods often distort fine-grained textural
details from the reference image, despite achieving high overall image quality.
We attribute these distortions to inadequate attention to corresponding regions
in the reference image. To address this, we thereby propose learning flow
fields in attention (Leffa), which explicitly guides the target query to attend
to the correct reference key in the attention layer during training.
Specifically, it is realized via a regularization loss on top of the attention
map within a diffusion-based baseline. Our extensive experiments show that
Leffa achieves state-of-the-art performance in controlling appearance (virtual
try-on) and pose (pose transfer), significantly reducing fine-grained detail
distortion while maintaining high image quality. Additionally, we show that our
loss is model-agnostic and can be used to improve the performance of other
diffusion models.