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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.04035 |
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| _version_ | 1866918488913739776 |
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| author | Ntavelis, Evangelos Wu, Sean Shahbazi, Mohamad Maninchedda, Fabio Kostiaev, Dmitry Sevastopolsky, Artem Megaro, Vittorio Phillips, Trevor Blumentals, Alejandro Ravikumar, Shridhar Gupta, Mehak Knothe, Reinhard Bayer, Jeronimo Vestner, Matthias Schaefer, Simon Etterlin, Thomas Zimmermann, Christian Deschler, Mathias Kaufmann, Peter Brugger, Stefan Martin, Sebastian Amberg, Brian Runia, Tom |
| author_facet | Ntavelis, Evangelos Wu, Sean Shahbazi, Mohamad Maninchedda, Fabio Kostiaev, Dmitry Sevastopolsky, Artem Megaro, Vittorio Phillips, Trevor Blumentals, Alejandro Ravikumar, Shridhar Gupta, Mehak Knothe, Reinhard Bayer, Jeronimo Vestner, Matthias Schaefer, Simon Etterlin, Thomas Zimmermann, Christian Deschler, Mathias Kaufmann, Peter Brugger, Stefan Martin, Sebastian Amberg, Brian Runia, Tom |
| contents | We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder architecture that compresses input views into a compact latent representation. This latent representation is then decoded into a set of UV-parameterized 3D Gaussians anchored to a neutral head template. This UV representation decouples the number of 3D Gaussians from the number and resolution of input images, enabling training with many high-resolution input views. We train and evaluate our model on an internal dataset with more than 10,000 subjects, which is an order of magnitude larger than existing multi-view human head datasets. HeadsUp achieves state-of-the-art reconstruction quality and generalizes to novel identities without test-time optimization. We extensively analyze the scaling behavior of our model across identities, views, and model capacity, revealing practical insights for quality-compute trade-offs. Finally, we highlight the strength of our latent space by showcasing two downstream applications: generating novel 3D identities and animating the 3D heads with expression blendshapes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures Ntavelis, Evangelos Wu, Sean Shahbazi, Mohamad Maninchedda, Fabio Kostiaev, Dmitry Sevastopolsky, Artem Megaro, Vittorio Phillips, Trevor Blumentals, Alejandro Ravikumar, Shridhar Gupta, Mehak Knothe, Reinhard Bayer, Jeronimo Vestner, Matthias Schaefer, Simon Etterlin, Thomas Zimmermann, Christian Deschler, Mathias Kaufmann, Peter Brugger, Stefan Martin, Sebastian Amberg, Brian Runia, Tom Computer Vision and Pattern Recognition Machine Learning We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder architecture that compresses input views into a compact latent representation. This latent representation is then decoded into a set of UV-parameterized 3D Gaussians anchored to a neutral head template. This UV representation decouples the number of 3D Gaussians from the number and resolution of input images, enabling training with many high-resolution input views. We train and evaluate our model on an internal dataset with more than 10,000 subjects, which is an order of magnitude larger than existing multi-view human head datasets. HeadsUp achieves state-of-the-art reconstruction quality and generalizes to novel identities without test-time optimization. We extensively analyze the scaling behavior of our model across identities, views, and model capacity, revealing practical insights for quality-compute trade-offs. Finally, we highlight the strength of our latent space by showcasing two downstream applications: generating novel 3D identities and animating the 3D heads with expression blendshapes. |
| title | Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.04035 |