@inproceedings{
seo2024genwarp,
title={GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping},
author={Junyoung Seo and Kazumi Fukuda and Takashi Shibuya and Takuya Narihira and Naoki Murata and Shoukang Hu and Chieh-Hsin Lai and Seungryong Kim and Yuki Mitsufuji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=rLJisJmMKw}
}
Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth estimation (MDE) has shown promise in handling in-the-wild images. In these methods, an input view is geometrically warped to novel views with estimated depth maps, then the warped image is inpainted by T2I models. However, they struggle with noisy depth maps and loss of semantic details when warping an input view to novel viewpoints. In this paper, we propose a novel approach for single-shot novel view synthesis, a semantic-preserving generative warping framework that enables T2I generative models to learn where to warp and where to generate, through augmenting cross-view attention with self-attention. Our approach addresses the limitations of existing methods by conditioning the generative model on source view images and incorporating geometric warping signals. Qualitative and quantitative evaluations demonstrate that our model outperforms existing methods in both in-domain and out-of-domain scenarios. Project page is available at https://GenWarp-NVS.github.io.
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping
Junyoung Seo Kazumi Fukuda Takashi Shibuya Takuya Narihira Naoki Murata Shoukang Hu Chieh-Hsin Lai Seungryong Kim Yuki Mitsufuji