We present SENS, a novel method for generating and editing 3D models from
hand-drawn sketches, including those of an abstract nature. Our method allows
users to quickly and easily sketch a shape, and then maps the sketch into the
latent space of a part-aware neural implicit shape architecture. SENS analyzes
the sketch and encodes its parts into ViT patch encoding, then feeds them into
a transformer decoder that converts them to shape embeddings, suitable for
editing 3D neural implicit shapes. SENS not only provides intuitive
sketch-based generation and editing, but also excels in capturing the intent of
the user's sketch to generate a variety of novel and expressive 3D shapes, even
from abstract sketches. We demonstrate the effectiveness of our model compared
to the state-of-the-art using objective metric evaluation criteria and a
decisive user study, both indicating strong performance on sketches with a
medium level of abstraction. Furthermore, we showcase its intuitive
sketch-based shape editing capabilities.

提出了一种名为 SENS 的方法，通过手绘草图生成和编辑 3D 模型，同时采用神经隐式形状架构，ViT 编码和转换器解码器来实现操作，具有较高的效率和性能。

基于素描的隐式神经形状建模

SENS: Sketch-based Implicit Neural Shape Modeling

In this paper, we explore the task of generating photo-realistic face images
from hand-drawn sketches. Existing image-to-image translation methods require a
large-scale dataset of paired sketches and images for supervision. They
typically utilize synthesized edge maps of face images as training data.
However, these synthesized edge maps strictly align with the edges of the
corresponding face images, which limit their generalization ability to real
hand-drawn sketches with vast stroke diversity. To address this problem, we
propose DeepFacePencil, an effective tool that is able to generate
photo-realistic face images from hand-drawn sketches, based on a novel dual
generator image translation network during training. A novel spatial attention
pooling (SAP) is designed to adaptively handle stroke distortions which are
spatially varying to support various stroke styles and different levels of
details. We conduct extensive experiments and the results demonstrate the
superiority of our model over existing methods on both image quality and model
generalization to hand-drawn sketches.

DeepFacePencil 是一种有效的工具，可以基于新颖的双生成器图像翻译网络和新颖的空间注意池设计来自动处理手绘草图的空间变形并支持不同的笔画风格和不同级别的细节，从而生成逼真的面部图像。