High-Fidelity Clothed Avatar Reconstruction from a Single Image
This work addresses the challenge of creating detailed 3D avatars for applications like virtual reality or gaming, but it is incremental as it builds on existing methods.
The paper tackles the problem of reconstructing high-fidelity 3D clothed avatars from a single image by combining learning-based and optimization-based methods in a coarse-to-fine framework, achieving efficient and accurate results as demonstrated in experiments on various datasets.
This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence o f the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes.