arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.01490cs.CV

面向基于神经渲染的VR远程临场与直播中人脸实时可视化流程

Towards a Pipeline for Real-Time Visualization of Faces for VR-based Telepresence and Live Broadcasting Utilizing Neural Rendering

University of Applied Sciences Düsseldorf(杜塞尔多夫应用科学大学);RheinMain University of Applied Sciences(莱茵美因应用科学大学)

Philipp Ladwig, Rene Ebertowski, Alexander Pech, Ralf Dörner, Christian Geiger

AI总结:

针对VR中HMD遮挡面部导致实时重建困难的问题,提出一种基于GAN的端到端流程,利用RGBD数据离线训练,在单GPU普通电脑上实时生成正面2.5D点云,实现低成本高质量重建,但未学习表情会产生伪影。

摘要:

虽然用于虚拟现实(VR)的头戴式显示器(HMD)已在消费市场上广泛普及,但它们对VR中实现逼真的面对面交谈构成了相当大的障碍,因为HMD遮挡了参与者面部的很大一部分。即使使用直接安装在HMD上的摄像头图像流,由于极端的拍摄角度和宽视场导致的强烈镜头畸变,拼接出令人信服的完整面部图像仍然是一项具有挑战性的任务。与VR领域长期的研究相比,对隐藏在HMD下的面部进行重建是一个非常新的研究课题。虽然当前最先进的解决方案展示了照片级逼真的3D重建结果,但它们需要高成本的实验室设备和巨大的计算开销。我们提出了一种专注于低成本硬件的方法,并且可以在配备单个GPU的普通游戏计算机上使用。我们利用生成对抗网络(GAN)的优势,构建了一个端到端的流程。我们的GAN基于RGBD相机捕获的训练数据集,生成正面朝向的2.5D点云。在我们的方法中,训练过程是离线的,而重建则实时运行。我们的结果表明,在“已学习”的表情范围内,重建质量足够。网络未学习到的表情会产生伪影,并可能引发恐怖谷效应。

英文摘要:

While head-mounted displays (HMDs) for Virtual Reality (VR) have become widely available in the consumer market, they pose a considerable obstacle for a realistic face-to-face conversation in VR since HMDs hide a significant portion of the participants faces. Even with image streams from cameras directly attached to an HMD, stitching together a convincing image of an entire face remains a challenging task because of extreme capture angles and strong lens distortions due to a wide field of view. Compared to the long line of research in VR, reconstruction of faces hidden beneath an HMD is a very recent topic of research. While the current state-of-the-art solutions demonstrate photo-realistic 3D reconstruction results, they require high-cost laboratory equipment and large computational costs. We present an approach that focuses on low-cost hardware and can be used on a commodity gaming computer with a single GPU. We leverage the benefits of an end-to-end pipeline by means of Generative Adversarial Networks (GAN). Our GAN produces a frontal-facing 2.5D point cloud based on a training dataset captured with an RGBD camera. In our approach, the training process is offline, while the reconstruction runs in real-time. Our results show adequate reconstruction quality within the 'learned' expressions. Expressions not learned by the network produce artifacts and can trigger the Uncanny Valley effect.

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。

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