  In the field of digital content creation, generating high-quality 3D
characters from single images is challenging, especially given the complexities
of various body poses and the issues of self-occlusion and pose ambiguity. In
this paper, we present CharacterGen, a framework developed to efficiently
generate 3D characters. CharacterGen introduces a streamlined generation
pipeline along with an image-conditioned multi-view diffusion model. This model
effectively calibrates input poses to a canonical form while retaining key
attributes of the input image, thereby addressing the challenges posed by
diverse poses. A transformer-based, generalizable sparse-view reconstruction
model is the other core component of our approach, facilitating the creation of
detailed 3D models from multi-view images. We also adopt a
texture-back-projection strategy to produce high-quality texture maps.
Additionally, we have curated a dataset of anime characters, rendered in
multiple poses and views, to train and evaluate our model. Our approach has
been thoroughly evaluated through quantitative and qualitative experiments,
showing its proficiency in generating 3D characters with high-quality shapes
and textures, ready for downstream applications such as rigging and animation.
