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ViTPose - A human pose estimation model based on the Transformer architecture

ViTPose is a human pose estimation model based on the Transformer architecture. It uses a standard visual Transformer as its backbone network, extracting features by segmenting the input image and feeding it into Transformer blocks, then...

What is ViTPose?

ViTPose is a human pose estimation model based on the Transformer architecture. Using a standard visual Transformer as its backbone, it extracts features by segmenting the input image and feeding it into Transformer blocks. These features are then decoded into heatmaps by a decoder, enabling accurate localization of human keypoints. The ViTPose series of models offers various scales, such as ViTPose-B, ViTPose-L, and ViTPose-H, allowing for selection based on different needs. It performs exceptionally well on datasets like MS COCO, demonstrating the powerful potential of a simple visual Transformer for pose estimation tasks. Furthermore, ViTPose+, as an improved version, extends to various body pose estimation tasks, covering different types of keypoints, including those for animals and humans, further enhancing performance and applicability.

ViTPose's main functions

  • Human body key point localizationIt can identify key points of the human body in images, such as joints, hands, and feet, and is widely used in motion analysis, virtual reality, human-computer interaction and other fields.
  • Simple model architectureThis method uses a standard visual Transformer as the backbone network for feature extraction, and then decodes the features into heatmaps using a simple decoder to achieve accurate localization of key points. Its model structure is simple, easy to implement, and easy to extend.
  • Highly scalableBy adjusting hyperparameters such as the number of layers and heads of the Transformer, the model can be scaled from 100M to 1B parameters to adapt to task requirements of different scales while maintaining high performance.
  • High flexibilityIt is flexible in training paradigms, supports different pre-training and fine-tuning strategies, as well as multiple input resolutions and attention types, and can handle a variety of pose estimation tasks.
  • Knowledge transferableKnowledge from large models can be easily transferred to smaller models using simple knowledge tokens, further enhancing the practicality and flexibility of the models.

ViTPose's technical principles

  • Visual TransformerViTPose uses a standard, non-hierarchical visual Transformer as its backbone network for feature extraction. The input image is first segmented into multiple patches, and each patch is embedded into a high-dimensional space to form tokens. These tokens are then processed through multiple Transformer layers, each containing a multi-head self-attention (MHSA) and a feed-forward network (FFN).
  • Feature extractionAfter processing by the Transformer layer, the final output feature map has rich semantic information and can capture the key features of the human body in the image.
  • Heatmap PredictionViTPose's decoder converts the feature map output by the encoder into a heatmap. Each pixel value in the heatmap represents the probability that the location is a keypoint. The decoder has two options:
    • Standard decoderUpsampling is performed using transposed convolution, and then a heatmap is generated through a prediction layer.
    • Simple Decoder: Use bilinear interpolation directly for upsampling to generate a heatmap.
  • Model transferViTPose's knowledge can be easily transferred to small models using simple knowledge tokens, further enhancing the models' usability and flexibility.
  • SOTA performanceViTPose has achieved new state-of-the-art (SOTA) and Pareto frontiers on multiple pose estimation datasets.

ViTPose project address

Application scenarios of ViTPose

  • Human pose estimationIt is mainly used to identify key points of the human body in images, such as joints, hands, and feet, and is widely used in motion analysis, virtual reality, human-computer interaction and other fields.
  • Animal posture estimationViTPose+ extends to animal pose estimation tasks and can be used for wildlife behavior research, pet behavior analysis, and more.
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