iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis

• URL: http://arxiv.org/abs/2310.16167v1
• Date: Tue, 24 Oct 2023 20:33:19 GMT
• Title: iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis
• Authors: Yash Kant, Aliaksandr Siarohin, Michael Vasilkovsky, Riza Alp Guler, Jian Ren, Sergey Tulyakov, Igor Gilitschenski
• Abstract summary: We present a method for generating consistent novel views from a single source image. Our approach focuses on maximizing the reuse of visible pixels from the source image. We use a monocular depth estimator that transfers visible pixels from the source view to the target view.
• Score: 45.88928345042103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a method for generating consistent novel views from a single source image. Our approach focuses on maximizing the reuse of visible pixels from the source image. To achieve this, we use a monocular depth estimator that transfers visible pixels from the source view to the target view. Starting from a pre-trained 2D inpainting diffusion model, we train our method on the large-scale Objaverse dataset to learn 3D object priors. While training we use a novel masking mechanism based on epipolar lines to further improve the quality of our approach. This allows our framework to perform zero-shot novel view synthesis on a variety of objects. We evaluate the zero-shot abilities of our framework on three challenging datasets: Google Scanned Objects, Ray Traced Multiview, and Common Objects in 3D. See our webpage for more details: https://yashkant.github.io/invs/

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