arXiv:2605.04035v3 Announce Type: replace-cross Abstract: 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
Advances in AI, particularly in generative models and 3D reconstruction, coupled with increasing computational power, enable more sophisticated and efficient methods for creating realistic digital humans.
High-quality 3D head reconstruction is crucial for the advancement of virtual reality, augmented reality, realistic avatars, telepresence, digital humans, and potentially humanoid robotics, reducing the gap between digital and physical representation.
The ability to reconstruct high-quality 3D heads at scale and from multi-view captures using an efficient feed-forward method streamlines the creation of digital human assets, making the process faster, more accessible, and more realistic.
- · Metaverse platforms
- · Gaming industry
- · Film and animation studios
- · Digital avatar services
- · Traditional 3D modeling workflows
- · Low-quality avatar providers
More realistic and personalized digital avatars become commonplace across various online platforms and applications.
The demand for sophisticated multi-camera capture setups increases, alongside the development of specialized hardware and software for 3D Gaussian Splatting.
Enhanced realism in digital interactions could blur the lines between virtual and physical identity, leading to new societal norms and ethical considerations around digital personhood.
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Read at arXiv cs.LG