This paper presents a novel non-rigid point set registration method that is
inspired by unsupervised clustering analysis. Unlike previous approaches that
treat the source and target point sets as separate entities, we develop a
holistic framework where they are formulated as clustering centroids and
clustering members, separately. We then adopt Tikhonov regularization with an
$\ell_1$-induced Laplacian kernel instead of the commonly used Gaussian kernel
to ensure smooth and more robust displacement fields. Our formulation delivers
closed-form solutions, theoretical guarantees, independence from dimensions,
and the ability to handle large deformations. Subsequently, we introduce a
clustering-improved Nystr\"om method to effectively reduce the computational
complexity and storage of the Gram matrix to linear, while providing a rigorous
bound for the low-rank approximation. Our method achieves high accuracy results
across various scenarios and surpasses competitors by a significant margin,
particularly on shapes with substantial deformations. Additionally, we
demonstrate the versatility of our method in challenging tasks such as shape
transfer and medical registration.

该论文介绍了一种受无监督聚类分析启发的新型非刚性点集配准方法，通过聚类中心和聚类成员分别将源点集和目标点集表述为一个整体框架，并采用带有 Ι₁- 诱导 Laplacian 核的 Tikhonov 正则化来确保平滑而更强健的位移场，在保证封闭形式解、理论保证、与维度无关以及处理大形变的能力的同时，引入了改进的聚类 Nyström 方法来有效减少计算复杂度和 Gram 矩阵的存储，同时为低秩逼近提供了严格的界限。我们的方法在各种场景中实现了高精度的结果，并在具有显著形变的形状上明显超过竞争对手，在形状转换和医学配准等具有挑战性的任务中展现了方法的多功能性。

无需对应的非刚性点集配准方法：基于无监督聚类分析

Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised  Clustering Analysis

In this paper, we propose a novel method for single-view 3D style transfer
that generates a unique 3D object with both shape and texture transfer. Our
focus lies primarily on birds, a popular subject in 3D reconstruction, for
which no existing single-view 3D transfer methods have been developed.The
method we propose seeks to generate a 3D mesh shape and texture of a bird from
two single-view images. To achieve this, we introduce a novel shape transfer
generator that comprises a dual residual gated network (DRGNet), and a
multi-layer perceptron (MLP). DRGNet extracts the features of source and target
images using a shared coordinate gate unit, while the MLP generates spatial
coordinates for building a 3D mesh. We also introduce a semantic UV texture
transfer module that implements textural style transfer using semantic UV
segmentation, which ensures consistency in the semantic meaning of the
transferred regions. This module can be widely adapted to many existing
approaches. Finally, our method constructs a novel 3D bird using a
differentiable renderer. Experimental results on the CUB dataset verify that
our method achieves state-of-the-art performance on the single-view 3D style
transfer task. Code is available in
this https URL

本文提出了一种新方法，用于单视角 3D 风格转换，生成具有形状和纹理转换的独特 3D 对象，主要关注鸟类的 3D 重建，通过引入 DRGNet 和 MLP 构建了一个新型的 3D 鸟类，并使用语义 UV 纹理传输模块实现文本样式转换，最终结果在 CUB 数据集上表现出最先进的性能。

创意鸟：无监督单视角三维风格转移

Creative Birds: Self-Supervised Single-View 3D Style Transfer

To enable realistic shape (e.g. pose and expression) transfer, existing face
reenactment methods rely on a set of target faces for learning subject-specific
traits. However, in real-world scenario end-users often only have one target
face at hand, rendering existing methods inapplicable. In this work, we bridge
this gap by proposing a novel one-shot face reenactment learning framework. Our
key insight is that the one-shot learner should be able to disentangle and
compose appearance and shape information for effective modeling. Specifically,
the target face appearance and the source face shape are first projected into
latent spaces with their corresponding encoders. Then these two latent spaces
are associated by learning a shared decoder that aggregates multi-level
features to produce the final reenactment results. To further improve the
synthesizing quality on mustache and hair regions, we additionally propose
FusionNet which combines the strengths of our learned decoder and the
traditional warping method. Extensive experiments show that our one-shot face
reenactment system achieves superior transfer fidelity as well as identity
preserving capability than alternatives. More remarkably, our approach trained
with only one target image per subject achieves competitive results to those
using a set of target images, demonstrating the practical merit of this work.
Code, models and an additional set of reenacted faces have been publicly
released at the project page.

该研究提出了一种基于一次学习的人脸复现系统，通过将目标人脸外观和源脸形状投影到潜在空间中，并学习一个共享解码器来使这两个潜在空间相关联，从而实现有效建模。该方法在 Mustache 和 Hair 区域照片合成的质量上比传统方法有很大提高，并且仅用一张目标图像训练便能实现与多张目标图像训练同样令人满意的结果.