The invention discloses a non-rigid object three-dimensional
reconstruction method based on a single-view neural
radiation field, and the method comprises the steps: firstly inputting a single
RGB image, extracting global features through a pre-trained
deep learning backbone network, and generating a
mask image of an object in combination with a semantic segmentation network; secondly, predicting
volume density and color distribution by adopting a neural
radiation field variant pixelNeRF, embedding a dynamic deformation field and a parameterized template to constrain shape rationality, and solving the problem of shape
ambiguity in single-view reconstruction; thirdly, extracting a grid model through a Marking Cubes
algorithm, complementing shielding and details by using a
reconstruction algorithm, and enhancing the sense of reality of the surface by using a texture super-resolution technology; and finally, providing a user
interaction interface for local optimization, and correcting a motion posture through a differentiable physical engine to generate a drivable three-dimensional
animation model. By combining the single-view neural
radiation field and the dynamic deformation modeling technology, the method has the advantages of high precision, high efficiency, support of
interactive editing, capability of generating driving animations and the like.