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arXiv · 2312.16218

Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks

Christian Simon·Sen He·Juan-Manuel Perez-Rua·Mengmeng Xu·Amine Benhalloum·Tao Xiang
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Abstract

Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability. To overcome the limitations of existing approaches regarding generalization and consistency, we introduce a novel neural rendering technique. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Specifically, our method builds neural encoding volumes from generated multi-view inputs. We adjust the weights of the SDF network conditioned on an input image at test-time to allow model adaptation to novel scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts derived from the synthesized views, we propose the use of a volume transformer module to improve the aggregation of image features instead of processing each viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we avoid the bottleneck of scene-specific optimization and maintain consistency across the images generated from multiple viewpoints. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.

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Christian Simon, Sen He, Juan-Manuel Perez-Rua, Mengmeng Xu, Amine Benhalloum, Tao Xiang. 2023-12-24. Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks. https://arxiv.org/abs/2312.16218

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