  Multi-modal domain translation typically refers to synthesizing a novel image
that inherits certain localized attributes from a 'content' image (e.g. layout,
semantics, or geometry), and inherits everything else (e.g. texture, lighting,
sometimes even semantics) from a 'style' image. The dominant approach to this
task is attempting to learn disentangled 'content' and 'style' representations
from scratch. However, this is not only challenging, but ill-posed, as what
users wish to preserve during translation varies depending on their goals.
Motivated by this inherent ambiguity, we define 'content' based on conditioning
information extracted by off-the-shelf pre-trained models. We then train our
style extractor and image decoder with an easy to optimize set of
reconstruction objectives. The wide variety of high-quality pre-trained models
available and simple training procedure makes our approach straightforward to
apply across numerous domains and definitions of 'content'. Additionally it
offers intuitive control over which aspects of 'content' are preserved across
domains. We evaluate our method on traditional, well-aligned, datasets such as
CelebA-HQ, and propose two novel datasets for evaluation on more complex
scenes: ClassicTV and FFHQ-Wild. Our approach, Sensorium, enables higher
quality domain translation for more complex scenes.
