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- W4361866138 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." @default.
- W4361866138 created "2023-04-05" @default.
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- W4361866138 date "2023-03-30" @default.
- W4361866138 modified "2023-09-27" @default.
- W4361866138 title "Consistent View Synthesis with Pose-Guided Diffusion Models" @default.
- W4361866138 doi "https://doi.org/10.48550/arxiv.2303.17598" @default.
- W4361866138 hasPublicationYear "2023" @default.
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