1D-DiffNPHR: 1D Diffusion Neural Parametric Head Reconstruction using a single image

1D-DiffNPHR: 1D Diffusion Neural Parametric Head Reconstruction using a single image
Pragati Jaiswal, Tewodros Amberbir Habtegebrial, Didier Stricker
In: Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2025), February 23-25, Porto, Portugal, SCITEPRESS (Science and Technology Publications, Lda), 2025.

Abstract:

In the field of 3D reconstruction, recent developments, especially in face reconstruction, have shown considerable promise. Despite these achievements, many of these techniques depend heavily on a large number of input views and are inefficient limiting their practicality. This paper proposes a solution to these challenges by focusing on single-view, full 3D head reconstruction. Our approach leverages a 1D diffusion model in combination with RGB image features and a neural parametric latent representation. Specifically, we train a system to learn latent codes conditioned on features extracted from a single input image. The model directly processes the input image at inference to generate latent codes, which are then decoded into a 3D mesh. Our method achieves high-fidelity reconstructions that outperform state-of-the-art approaches such as 3D Morphable Models, Neural Parametric Head Models, and existing methods for head reconstruction.