DeepSketchHair: Deep Sketch-based 3D Hair Modeling

TL;DR

DeepSketchHair is an interactive deep learning system that converts 2D sketches into detailed 3D hair models, enabling easy editing and customization from user sketches.

Contribution

The paper introduces a novel neural network pipeline for sketch-based 3D hair modeling, integrating multiple networks trained on synthetic data for accurate and editable results.

Findings

Accurately generates 3D hair models from simple sketches.

Supports editing and updating hair models with new sketches.

Outperforms prior methods in realism and user control.

Abstract

We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally and locally. The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field. Our system also supports hair editing with additional sketches in new views. This is enabled by another deep neural network, V2VNet, which updates the 3D vector field with respect to the new sketches. All the three networks are trained with synthetic data generated from a 3D hairstyle database. We demonstrate the effectiveness and expressiveness of our tool using a variety of hairstyles and also compare our method with prior art.