TwoSquared: 4D Generation from 2D Image Pairs
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915248478355456 |
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| author | Sang, Lu Canfes, Zehranaz Cao, Dongliang Marin, Riccardo Bernard, Florian Cremers, Daniel |
| author_facet | Sang, Lu Canfes, Zehranaz Cao, Dongliang Marin, Riccardo Bernard, Florian Cremers, Daniel |
| contents | Despite the astonishing progress in generative AI, 4D dynamic object generation remains an open challenge. With limited high-quality training data and heavy computing requirements, the combination of hallucinating unseen geometry together with unseen movement poses great challenges to generative models. In this work, we propose TwoSquared as a method to obtain a 4D physically plausible sequence starting from only two 2D RGB images corresponding to the beginning and end of the action. Instead of directly solving the 4D generation problem, TwoSquared decomposes the problem into two steps: 1) an image-to-3D module generation based on the existing generative model trained on high-quality 3D assets, and 2) a physically inspired deformation module to predict intermediate movements. To this end, our method does not require templates or object-class-specific prior knowledge and can take in-the-wild images as input. In our experiments, we demonstrate that TwoSquared is capable of producing texture-consistent and geometry-consistent 4D sequences only given 2D images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TwoSquared: 4D Generation from 2D Image Pairs Sang, Lu Canfes, Zehranaz Cao, Dongliang Marin, Riccardo Bernard, Florian Cremers, Daniel Computer Vision and Pattern Recognition Despite the astonishing progress in generative AI, 4D dynamic object generation remains an open challenge. With limited high-quality training data and heavy computing requirements, the combination of hallucinating unseen geometry together with unseen movement poses great challenges to generative models. In this work, we propose TwoSquared as a method to obtain a 4D physically plausible sequence starting from only two 2D RGB images corresponding to the beginning and end of the action. Instead of directly solving the 4D generation problem, TwoSquared decomposes the problem into two steps: 1) an image-to-3D module generation based on the existing generative model trained on high-quality 3D assets, and 2) a physically inspired deformation module to predict intermediate movements. To this end, our method does not require templates or object-class-specific prior knowledge and can take in-the-wild images as input. In our experiments, we demonstrate that TwoSquared is capable of producing texture-consistent and geometry-consistent 4D sequences only given 2D images. |
| title | TwoSquared: 4D Generation from 2D Image Pairs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.12825 |