Method and apparatus for synthesizing realistic hand poses based on blending generative adversarial networks
A hand, image technique used in the field of generating realistic hand poses
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[0021] Embodiments of the present application relate to generating realistic hand gestures. Existing hand pose estimation algorithms can be greatly improved by augmenting the training data with generated hand poses that are naturally annotated with ground truth. Specifically, an augmented reality simulator can synthesize hand poses with accurate 3D hand keypoint annotations. These synthetic hand poses may look unnatural and are not suitable for training. Although the synthesized hand poses come with precise joint labels, however, they look unnatural and unsuitable for training.
[0022] In order to generate more realistic hand poses, in the embodiment of the present application, each synthetic hand pose is blended with the real background, and a hybrid generative adversarial network (BlendGAN) is developed, which can align the synthetic hand poses with the real background tonal and color distributions, and can generate high-quality hand poses.
[0023] figure 1 An overview...
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