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Hallucinating Pose-Compatible Scenes

ECCV 2022

Tim Brooks, Alexei A. Efros

Abstract

What does human pose tell us about a scene? We propose a task to answer this question: given human pose as input, hallucinate a compatible scene. Subtle cues captured by human pose — action semantics, environment affordances, object interactions — provide surprising insight into which scenes are compatible. We present a large-scale generative adversarial network for pose-conditioned scene generation. We significantly scale the size and complexity of training data, curating a massive meta-dataset containing over 19 million frames of humans in everyday environments. We double the capacity of our model with respect to StyleGAN2 to handle such complex data, and design a pose conditioning mechanism that drives our model to learn the nuanced relationship between pose and scene. We leverage our trained model for various applications: hallucinating pose-compatible scene(s) with or without humans, visualizing incompatible scenes and poses, placing a person from one generated image into another scene, and animating pose. Our model produces diverse samples and outperforms pose-conditioned StyleGAN2 and Pix2Pix baselines in terms of accurate human placement (percent of correct keypoints) and image quality (Fréchet inception distance).

ArXiv: https://arxiv.org/abs/2112.06909

GitHub: https://github.com/timothybrooks/hallucinating-scenes

@inproceedings{brooks2022hallucinating,
  title={Hallucinating Pose-Compatible Scenes},
  author={Brooks, Tim and Efros, Alexei A},
  booktitle={European Conference on Computer Vision},
  year={2022}
}