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arXiv · 2310.16167

iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis

Yash Kant·Aliaksandr Siarohin·Michael Vasilkovsky·Riza Alp Guler·Jian Ren·Sergey Tulyakov·Igor Gilitschenski
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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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Yash Kant, Aliaksandr Siarohin, Michael Vasilkovsky, Riza Alp Guler, Jian Ren, Sergey Tulyakov, Igor Gilitschenski. 2023-10-24. iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis. https://arxiv.org/abs/2310.16167

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