  The success of style-based generators largely benefits from style modulation,
which helps take care of the cross-instance variation within data. However, the
instance-wise stochasticity is typically introduced via regular convolution,
where kernels interact with features at some fixed locations, limiting its
capacity for modeling geometric variation. To alleviate this problem, we equip
the generator in generative adversarial networks (GANs) with a plug-and-play
module, termed as modulated transformation module (MTM). This module predicts
spatial offsets under the control of latent codes, based on which the
convolution operation can be applied at variable locations for different
instances, and hence offers the model an additional degree of freedom to handle
geometry deformation. Extensive experiments suggest that our approach can be
faithfully generalized to various generative tasks, including image generation,
3D-aware image synthesis, and video generation, and get compatible with
state-of-the-art frameworks without any hyper-parameter tuning. It is
noteworthy that, towards human generation on the challenging TaiChi dataset, we
improve the FID of StyleGAN3 from 21.36 to 13.60, demonstrating the efficacy of
learning modulated geometry transformation.
