在任意地点、以任意姿势合成任意人物
Synthesizing Anyone, Anywhere, in Any Pose
AI总结：
针对野外人体合成的难点，研究提出关键点引导的TriA-GAN，结合投影GAN与精心训练策略，以更少条件信息实现更优合成效果，且潜空间支持文本引导编辑。
摘要：
我们针对野外人体合成任务展开研究，该任务的核心目标是在任意图像的任意区域生成完整人体。野外人体合成长期以来都是一项颇具挑战性且探索不足的任务，现有方法难以应对极端姿势、遮挡物体以及复杂背景的问题。我们的主要贡献是TriA-GAN，这是一种关键点引导的GAN（生成对抗网络），能够在任意位置、以任意给定姿势合成任意人物。该方法的核心是将projected GANs（投影生成对抗网络）与精心设计的训练策略相结合，凭借简洁的生成器架构成功应对野外全身合成的诸多挑战。实验表明，TriA-GAN的性能显著优于以往的野外全身合成方法，同时合成所需的条件信息更少（仅需关键点，而非DensePose）。最后我们还验证了，TriA-GAN的潜空间兼容标准的无监督编辑技术，可实现对生成人体的文本引导编辑。
英文摘要：
We address the task of in-the-wild human figure synthesis, where the primary goal is to synthesize a full body given any region in any image. In-the-wild human figure synthesis has long been a challenging and under-explored task, where current methods struggle to handle extreme poses, occluding objects, and complex backgrounds. Our main contribution is TriA-GAN, a keypoint-guided GAN that can synthesize Anyone, Anywhere, in Any given pose. Key to our method is projected GANs combined with a well-crafted training strategy, where our simple generator architecture can successfully handle the challenges of in-the-wild full-body synthesis. We show that TriA-GAN significantly improves over previous in-the-wild full-body synthesis methods, all while requiring less conditional information for synthesis (keypoints \vs DensePose). Finally, we show that the latent space of TriA-GAN is compatible with standard unconditional editing techniques, enabling text-guided editing of generated human figures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
