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AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Prediction

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arxiv 2503.12929 v4 pith:RZRP3J6K submitted 2025-03-17 cs.CV

AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Prediction

Xuying Zhang , Yupeng Zhou , Kai Wang , Yikai Wang , Zhen Li , Shaohui Jiao , Daquan Zhou , Qibin Hou
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Ming-Ming Cheng
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classification cs.CV
keywords viewsinputgeneratednext-viewpredictionar-1-to-3consistencyencoding
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Novel view synthesis (NVS) is a cornerstone for image-to-3d creation. However, existing works still struggle to maintain consistency between the generated views and the input views, especially when there is a significant camera pose difference, leading to poor-quality 3D geometries and textures. We attribute this issue to their treatment of all target views with equal priority according to our empirical observation that the target views closer to the input views exhibit higher fidelity. With this inspiration, we propose AR-1-to-3, a novel next-view prediction paradigm based on diffusion models that first generates views close to the input views, which are then utilized as contextual information to progressively synthesize farther views. To encode the generated view subsequences as local and global conditions for the next-view prediction, we accordingly develop a stacked local feature encoding strategy (Stacked-LE) and an LSTM-based global feature encoding strategy (LSTM-GE). Extensive experiments demonstrate that our method significantly improves the consistency between the generated views and the input views, producing high-fidelity 3D assets.

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