DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras

• URL: http://arxiv.org/abs/2207.08000v2
• Date: Wed, 20 Jul 2022 08:12:00 GMT
• Title: DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras
• Authors: Ruizhi Shao, Zerong Zheng, Hongwen Zhang, Jingxiang Sun, Yebin Liu
• Abstract summary: We propose DiffuStereo, a novel system using only sparse cameras for high-quality 3D human reconstruction. At its core is a novel diffusion-based stereo module, which introduces diffusion models into the iterative stereo matching network. We present a multi-level stereo network architecture to handle high-resolution (up to 4k) inputs without requiring unaffordable memory footprint.
• Score: 33.6247548142638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose DiffuStereo, a novel system using only sparse cameras (8 in this work) for high-quality 3D human reconstruction. At its core is a novel diffusion-based stereo module, which introduces diffusion models, a type of powerful generative models, into the iterative stereo matching network. To this end, we design a new diffusion kernel and additional stereo constraints to facilitate stereo matching and depth estimation in the network. We further present a multi-level stereo network architecture to handle high-resolution (up to 4k) inputs without requiring unaffordable memory footprint. Given a set of sparse-view color images of a human, the proposed multi-level diffusion-based stereo network can produce highly accurate depth maps, which are then converted into a high-quality 3D human model through an efficient multi-view fusion strategy. Overall, our method enables automatic reconstruction of human models with quality on par to high-end dense-view camera rigs, and this is achieved using a much more light-weight hardware setup. Experiments show that our method outperforms state-of-the-art methods by a large margin both qualitatively and quantitatively.

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