Details

Autor(en) / Beteiligte

• Tseng, Hung-Yu
• Li, Qinbo
• Kim, Changil
• Alsisan, Suhib
• Huang, Jia-Bin
• Kopf, Johannes

Titel

Consistent View Synthesis with Pose-Guided Diffusion Models

Ist Teil von

• 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, p.16773-16783

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• 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. More qualitative results are available at https://poseguided-diffusion.github.io/.