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

christian simon, Sen He, Juanma Perez, Frost Xu, Amine Benhalloum, Tao Xiang

Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks: 8 upvotes on Hugging Face Daily Papers, #13 of 19 papers on 2023-12-29. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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