In this work, we present a novel learning based approach to reconstruct 3D faces from a single or multiple images. Our method uses a simple yet powerful architecture based on siamese neural networks that helps to extract relevant features from each view while keeping the models small. Instead of minimizing multiple objectives, we propose to simultaneously learn the 3D shape and the individual camera poses by using a single term loss based on the reprojection error, which generalizes from one to multiple views. This allows to globally optimize the whole scene without having to tune any hyperparameters and to achieve low reprojection errors, which are important for further texture generation. Finally, we train our model on a large scale dataset with more than 6,000 facial scans. We report competitive results in 3DFAW 2019 challenge, showing the effectiveness of our method.
Recent advancements in learning techniques that employ coordinate-based neural representations have yielded remarkable results in multi-view 3D reconstruction tasks. However, these approaches often require a substantial number of input views (typically several tens) and computationally intensive optimization procedures to achieve their effectiveness. In this paper, we address these limitations specifically for the problem of few-shot full 3D head reconstruction. We accomplish this by incorporating a probabilistic shape and appearance prior into coordinate-based representations, enabling faster convergence and improved generalization when working with only a few input images (even as low as a single image). During testing, we leverage this prior to guide the fitting process of a signed distance function using a differentiable renderer. By incorporating the statistical prior alongside parallelizable ray tracing and dynamic caching strategies, we achieve an efficient and accurate approach to few-shot full 3D head reconstruction. Moreover, we extend the H3DS dataset, which now comprises 60 high-resolution 3D full head scans and their corresponding posed images and masks, which we use for evaluation purposes. By leveraging this dataset, we demonstrate the remarkable capabilities of our approach in achieving state-of-the-art results in geometry reconstruction while being an order of magnitude faster than previous approaches.
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