Get shape, pose, and appearance from a single image using signed distance function with Pavllo
Get shape, pose, and appearance from a single image using signed distance function with Pavllo
Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion
arXiv paper abstract https://arxiv.org/abs/2211.11674
arXiv PDF paper https://arxiv.org/pdf/2211.11674.pdf
Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies.
Recent work in this area, however, has mostly focused on synthetic datasets where exact ground-truth poses are known, and has overlooked pose estimation, which is important for certain downstream applications such as augmented reality (AR) and robotics.
… introduce a principled end-to-end reconstruction framework for natural images, where accurate ground-truth poses are not available.
… approach recovers an SDF-parameterized 3D shape, pose, and appearance from a single image of an object, without exploiting multiple views during training.
More specifically, … leverage an unconditional 3D-aware generator, to which … apply a hybrid inversion scheme where a model produces a first guess of the solution which is then refined via optimization.
… framework can de-render an image in as few as 10 steps, enabling its use in practical scenarios … demonstrate state-of-the-art results on a variety of real and synthetic benchmarks.
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