Learning to Caricature via Semantic Shape Transform

Deep Deformable 3D Caricatures with Learned Shape Control

A 3D caricature is an exaggerated 3D depiction of a human face. The goal of this paper is to model the variations of 3D caricatures in a compact parameter space so that we can provide a useful data-driven toolkit for handling 3D caricature deformations. To achieve the goal, we propose an MLP-based framework for building a deformable surface model, which takes a latent code and produces a 3D surface. In the framework, a SIREN MLP models a function that takes a 3D position on a fixed template surface and returns a 3D displacement vector for the input position. We create variations of 3D surfaces…

CaricHarmony：身份保持的漫画合成的对比扩散路径

CaricHarmony: Contrastive Diffusion Paths for Identity-Preserving Caricature Synthesis

提出CaricHarmony，一种无需训练的方法，通过并行无污染扩散路径解决身份与形状条件信号污染问题，实现平衡的漫画合成，在保持身份一致性的同时达到最优形状保真度。

面向素描人脸识别的语义神经模型方法

semantic neural model approach for face recognition from sketch

针对当前人脸素描合成与识别多被分开研究的问题，提出一种可同时处理二者的语义神经模型方法，在正面无遮挡等假设下通过划分重叠人脸块学习，可应用于执法领域。

AutoToon: Automatic Geometric Warping for Face Cartoon Generation

Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promising results in capturing style and higher-level exaggerations. However, a key part of caricatures, face warping, has remained challenging for these systems. In this work, we propose AutoToon, the first supervised deep learning method that yields high-quality warps for the warpin…

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

Domain adaptation of visual detectors is a critical challenge, yet existing methods have overlooked pixel appearance transformations, focusing instead on bootstrapping and/or domain confusion losses. We propose a Semantic Pixel-Level Adaptation Transform (SPLAT) approach to detector adaptation that efficiently generates cross-domain image pairs. Our model uses aligned-pair and/or pseudo-label losses to adapt an object detector to the target domain, and can learn transformations with or without densely labeled data in the source (e.g. semantic segmentation annotations). Without dense labels, as…

Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected conditional random fields (CRFs) can significantly enhance their object localization accuracy, yet dense CRF inference is computationally expensive. We propose replacing the fully-connected CRF with domain transform (DT), a modern edge-preserving filtering method in which the amount of smoothing is controlled by a reference edge map. Domain transform filtering is several times faster than dense CRF inference and we sh…