In recent years, a plethora of diverse methods have been proposed for 3D pose
estimation. Among these, self-attention mechanisms and graph convolutions have
both been proven to be effective and practical methods. Recognizing the
strengths of those two techniques, we have developed a novel Semantic Graph
Attention Network which can benefit from the ability of self-attention to
capture global context, while also utilizing the graph convolutions to handle
the local connectivity and structural constraints of the skeleton. We also
design a Body Part Decoder that assists in extracting and refining the
information related to specific segments of the body. Furthermore, our approach
incorporates Distance Information, enhancing our model's capability to
comprehend and accurately predict spatial relationships. Finally, we introduce
a Geometry Loss who makes a critical constraint on the structural skeleton of
the body, ensuring that the model's predictions adhere to the natural limits of
human posture. The experimental results validate the effectiveness of our
approach, demonstrating that every element within the system is essential for
improving pose estimation outcomes. With comparison to state-of-the-art, the
proposed work not only meets but exceeds the existing benchmarks.

我们开发了一种新颖的语义图注意力网络，它能够从自注意力捕获全局上下文的能力中受益，同时利用图卷积来处理骨架的局部连通性和结构约束。我们还设计了一个身体部分解码器，用于提取和改进与身体特定部分相关的信息。此外，我们的方法还包含距离信息，增强了模型理解和准确预测空间关系的能力。最后，我们引入了一种几何损失，对身体的结构骨架施加了关键约束，以确保模型的预测符合人体姿势的自然限制。实验证明了我们方法的有效性，表明系统中的每个元素对提高姿势估计结果至关重要。与最先进方法相比，我们的工作不仅达到了现有基准，而且超过了现有基准。

基于语义图注意网络和距离信息的 3D 全身姿态估计

3D WholeBody Pose Estimation based on Semantic Graph Attention Network  and Distance Information

This paper presents an unpaired method for creating line drawings from
photographs. Current methods often rely on high quality paired datasets to
generate line drawings. However, these datasets often have limitations due to
the subjects of the drawings belonging to a specific domain, or in the amount
of data collected. Although recent work in unsupervised image-to-image
translation has shown much progress, the latest methods still struggle to
generate compelling line drawings. We observe that line drawings are encodings
of scene information and seek to convey 3D shape and semantic meaning. We build
these observations into a set of objectives and train an image translation to
map photographs into line drawings. We introduce a geometry loss which predicts
depth information from the image features of a line drawing, and a semantic
loss which matches the CLIP features of a line drawing with its corresponding
photograph. Our approach outperforms state-of-the-art unpaired image
translation and line drawing generation methods on creating line drawings from
arbitrary photographs. For code and demo visit our webpage
carolineec.github.io/informative_drawings

该文章提出一种从照片中创建线条图的无配对方法，该方法结合了几何和语义信息并使用了图像翻译和损失函数来创建场景信息和 3D 形状的编码。