We present a fully automatic framework that digitizes a complete 3D head with hair from a
single unconstrained image. Our system offers a practical and consumer-friendly end-to-end …

In this work, we tackle the challenging problem of learning-based single-view 3D hair
modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using …

Tremendous efforts have been made to learn animatable and photorealistic human avatars.
Towards this end, both explicit and implicit 3D representations are heavily studied for a …

We present a novel image-based representation for dynamic 3D avatars, which allows
effective handling of various hairstyles and headwear, and can generate expressive facial …

Abstract We present Neural Strands, a novel learning framework for modeling accurate hair
geometry and appearance from multi-view image inputs. The learned hair model can be …

Despite recent successes in hair acquisition that fits a high-dimensional hair model to a
specific input subject, generative hair models, which establish general embedding spaces …

Undoubtedly, high-fidelity 3D hair plays an indispensable role in digital humans. However,
existing monocular hair modeling methods are either tricky to deploy in digital systems (eg …

Despite the recent success of face image generation with GANs, conditional hair editing
remains challenging due to the under-explored complexity of its geometry and appearance …

We introduce CT2Hair, a fully automatic framework for creating high-fidelity 3D hair models
that are suitable for use in downstream graphics applications. Our approach utilizes real …

Hairstyle transfer is challenging due to hair structure differences in the source and target
hair. Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO) …