Scene Aware Person Image Generation through Global Contextual Conditioning

• URL: http://arxiv.org/abs/2206.02717v1
• Date: Mon, 6 Jun 2022 16:18:15 GMT
• Title: Scene Aware Person Image Generation through Global Contextual Conditioning
• Authors: Prasun Roy, Subhankar Ghosh, Saumik Bhattacharya, Umapada Pal, Michael Blumenstein
• Abstract summary: We propose a novel pipeline to generate and insert contextually relevant person images into an existing scene. More specifically, we aim to insert a person such that the location, pose, and scale of the person being inserted blends in with the existing persons in the scene.
• Score: 24.317541784957285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Person image generation is an intriguing yet challenging problem. However, this task becomes even more difficult under constrained situations. In this work, we propose a novel pipeline to generate and insert contextually relevant person images into an existing scene while preserving the global semantics. More specifically, we aim to insert a person such that the location, pose, and scale of the person being inserted blends in with the existing persons in the scene. Our method uses three individual networks in a sequential pipeline. At first, we predict the potential location and the skeletal structure of the new person by conditioning a Wasserstein Generative Adversarial Network (WGAN) on the existing human skeletons present in the scene. Next, the predicted skeleton is refined through a shallow linear network to achieve higher structural accuracy in the generated image. Finally, the target image is generated from the refined skeleton using another generative network conditioned on a given image of the target person. In our experiments, we achieve high-resolution photo-realistic generation results while preserving the general context of the scene. We conclude our paper with multiple qualitative and quantitative benchmarks on the results.

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