Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data

TL;DR

This paper introduces a novel unpaired training method for face portrait line drawing generation that uses a quality metric guided by human perception, enabling multi-style and unseen style synthesis while preserving facial features.

Contribution

The paper proposes a new quality metric and asymmetric cycle mapping approach for unpaired portrait drawing generation, improving style diversity and feature preservation.

Findings

Outperforms state-of-the-art methods in quality and style diversity

Successfully generates portrait drawings in unseen styles

Preserves important facial features effectively

Abstract

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i.e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data. To achieve these benefits, we (1) propose a novel quality metric for portrait drawings which is learned from human perception, and (2) introduce a quality loss to guide the network toward generating better looking portrait drawings. We observe that existing unpaired translation methods such as CycleGAN tend to embed invisible reconstruction information indiscriminately in the whole drawings due to significant information imbalance between the photo and portrait drawing domains, which leads to important facial features missing. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible and only embedded in the selected facial regions. Along with localized discriminators for important facial regions, our method well preserves all important facial features in the generated drawings. Generator dissection further explains that our model learns to incorporate face semantic information during drawing generation. Extensive experiments including a user study show that our model outperforms state-of-the-art methods.