TRELLIS: Image to 3D video

1. Tutorial Introduction

TRELLIS is a graph neural network-based interpretability framework developed by the Microsoft team in 2024. It aims to provide efficient model interpretability by learning the characteristics of graph structured data. The project combines graph neural networks (GNNs) and graph theory to provide a flexible architecture for interpreting and understanding the potential patterns and relationships of large-scale graph data.

Feature highlights:

Graph neural network model: Based on the architecture of modern graph neural network, it supports the analysis and learning of various graph types. Interpretability: Using model-based interpretability technology, it can provide a detailed understanding of the prediction results of graph data. Multiple algorithm support: Supports multiple graph models and algorithms, including graph convolutional network (GCN), graph attention network (GAT), etc. Efficient implementation: Optimized algorithm implementation supports efficient processing of large-scale graph data.

该教程使用单卡 4090 即可启动。

Effect example:

2. Operation steps

1. 启动容器，打开工作空间：

2. 在工作空间中双击打开终端：

3. 在打开的终端中输入指令：bash run.sh 后等待程序运行，待出现 8080 端口后即可在右侧打开 API 地址，如下图所示：
注意：打开 API 地址需要实名认证

4. 选择图片进行上传并生成相应的 3D 影像：
注意：图片上传后会自动提取图中对象，并转为相应格式，仅支持上传 .jpg/.png 格式图片

3. Introduction to core functions

Graph Neural Network Model
- Graph Convolutional Networks (GCN): GCN is a network structure based on node adjacency, suitable for node classification and regression tasks of large-scale graph data.
- Graph Attention Networks (GAT): GAT is a graph neural network based on self-attention mechanism, which can better handle the unbalanced relationship between nodes.
Model Interpretability
- Node importance analysis: Analyze the contribution and importance of each node to the final prediction through model interpretability technology.
- Edge Weight Interpretation: By learning the weights and connectivity of edges, we can gain a deeper understanding of the interactions between different nodes in the graph.
Efficient Implementation
- GPU acceleration: Leveraging modern graph computing frameworks, it supports GPU-based accelerated training and reasoning, significantly improving model training speed.
- Distributed computing: Supports parallel training on multi-node systems and processes larger data sets.

4. Exchange and Discussion

🖌️ If you find a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome everyone to scan the QR code to join the group, note [SD Tutorial], discuss technical issues with everyone, and share application results!