# Cross-Domain Image Manipulation by Demonstration

## TL;DR

This paper introduces a model capable of manipulating specific visual attributes of real images by learning from synthetic demonstrations, effectively isolating and altering properties like expression without labeled data.

## Contribution

The work presents a novel approach to attribute manipulation that works across domains without explicit labels, using synthetic examples to control real-world image attributes.

## Key findings

- Model successfully manipulates face expressions while preserving other attributes.
- Performance degrades with increased discrepancy in attribute distributions.
- Effective in real human face manipulation using nonphotorealistic 3D renders.

## Abstract

In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation. As an example, we train our model to manipulate the expression of a human face using nonphotorealistic 3D renders of a face with varied expression. Our model manages to preserve all other visual attributes of a real face, such as head orientation, even though this and other attributes are not labeled in either real or synthetic domain. Since our model learns to manipulate a specific property in isolation using only "synthetic demonstrations" of such manipulations without explicitly provided labels, it can be applied to shape, texture, lighting, and other properties that are difficult to measure or represent as real-valued vectors. We measure the degree to which our model preserves other attributes of a real image when a single specific attribute is manipulated. We use digit datasets to analyze how discrepancy in attribute distributions affects the performance of our model, and demonstrate results in a far more difficult setting: learning to manipulate real human faces using nonphotorealistic 3D renders.