Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models

• URL: http://arxiv.org/abs/2310.03020v2
• Date: Thu, 14 Mar 2024 23:21:46 GMT
• Title: Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
• Authors: Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, Heng Wang,
• Abstract summary: Consistent-1-to-3 is a generative framework that significantly mitigates this issue. We decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions. We propose to employ epipolor-guided attention to incorporate geometry constraints, and multi-view attention to better aggregate multi-view information.
• Score: 16.326276673056334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot novel view synthesis (NVS) from a single image is an essential problem in 3D object understanding. While recent approaches that leverage pre-trained generative models can synthesize high-quality novel views from in-the-wild inputs, they still struggle to maintain 3D consistency across different views. In this paper, we present Consistent-1-to-3, which is a generative framework that significantly mitigates this issue. Specifically, we decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions. We design a scene representation transformer and view-conditioned diffusion model for performing these two stages respectively. Inside the models, to enforce 3D consistency, we propose to employ epipolor-guided attention to incorporate geometry constraints, and multi-view attention to better aggregate multi-view information. Finally, we design a hierarchy generation paradigm to generate long sequences of consistent views, allowing a full 360-degree observation of the provided object image. Qualitative and quantitative evaluation over multiple datasets demonstrates the effectiveness of the proposed mechanisms against state-of-the-art approaches. Our project page is at https://jianglongye.com/consistent123/

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