Consistent View Synthesis with Pose-Guided Diffusion Models

• URL: http://arxiv.org/abs/2303.17598v1
• Date: Thu, 30 Mar 2023 17:59:22 GMT
• Title: Consistent View Synthesis with Pose-Guided Diffusion Models
• Authors: Hung-Yu Tseng, Qinbo Li, Changil Kim, Suhib Alsisan, Jia-Bin Huang, Johannes Kopf
• Abstract summary: Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications. We propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image.
• Score: 51.37925069307313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications that provide immersive experiences. However, most existing techniques can only synthesize novel views within a limited range of camera motion or fail to generate consistent and high-quality novel views under significant camera movement. In this work, we propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image. We design an attention layer that uses epipolar lines as constraints to facilitate the association between different viewpoints. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed diffusion model against state-of-the-art transformer-based and GAN-based approaches.

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