ViTPose is an open-source action estimation model that excels at recognizing human poses, much like understanding what actions you are performing. The model's greatest strength lies in its simplicity and efficiency; it does not use a complex network structure but instead directly employs a technique called Visual Transformer.
The core of ViTPose is the use of a pure Visual Transformer, which acts like a powerful "skeleton" to extract key features from images. Unlike other models, it does not require complex Convolutional Neural Networks (CNNs) for assistance. Its structure is very simple, consisting of multiple stacked Transformer layers.
The ViTPose model can be resized as needed. Like a flexible ruler, you can control the model's size by increasing or decreasing the number of Transformer layers, thereby finding a balance between performance and speed. You can also adjust the resolution of the input images, and the model will adapt. Additionally, it can handle multiple datasets simultaneously, meaning you can use it to recognize data from different poses.
Despite its simple structure, ViTPose performs exceptionally well in human pose estimation. It has achieved impressive results on the well-known MS COCO dataset, even surpassing many more complex models. This indicates that simple models can also be very powerful. Another feature of ViTPose is its ability to transfer "knowledge" from larger models to smaller ones. It's like an experienced teacher imparting knowledge to students, allowing smaller models to possess the capabilities of larger ones.
The code and model for ViTPose are open-source, meaning anyone can use it for free and conduct research and development based on it.
ViTPose is like a simple yet powerful tool that helps computers understand human actions. Its advantages include simplicity, flexibility, efficiency, and ease of learning. This makes it a very promising baseline model in the field of human pose estimation.
The model uses Transformer layers to process image data and employs a lightweight decoder to predict key points. The decoder can use simple deconvolution layers or bilinear interpolation to upsample feature maps. ViTPose not only performs well on standard datasets but also excels in handling occlusions and different poses. It can be applied to various tasks such as human pose estimation, animal pose estimation, and facial keypoint detection.
Demo: https://huggingface.co/spaces/hysts/ViTPose-transformers
Model: https://huggingface.co/collections/usyd-community/vitpose-677fcfd0a0b2b5c8f79c4335