StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair Geometric Priors (2412.11586v2)

Abstract: While haircut indicates distinct personality, existing avatar generation methods fail to model practical hair due to the general or entangled representation. We propose StrandHead, a novel text to 3D head avatar generation method capable of generating disentangled 3D hair with strand representation. Without using 3D data for supervision, we demonstrate that realistic hair strands can be generated from prompts by distilling 2D generative diffusion models. To this end, we propose a series of reliable priors on shape initialization, geometric primitives, and statistical haircut features, leading to a stable optimization and text-aligned performance. Extensive experiments show that StrandHead achieves the state-of-the-art reality and diversity of generated 3D head and hair. The generated 3D hair can also be easily implemented in the Unreal Engine for physical simulation and other applications. The code will be available at https://xiaokunsun.github.io/StrandHead.github.io.

• The paper introduces a strand-disentangled framework that uniquely generates detailed strand-based hair geometries from text using 2D diffusion models.
• The paper employs a differentiable prismatization technique to transform hair strands into watertight prismatic meshes, enhancing mesh renderability and detail.
• The paper integrates orientation and curvature regularization to faithfully reproduce realistic hair shapes, outperforming conventional avatar generation methods.

Overview of "StrandHead: Text to Strand-Disentangled 3D Head Avatars Using Hair Geometric Priors"

The paper under discussion introduces StrandHead, a sophisticated framework designed to generate realistic 3D head avatars from text descriptions, focusing on the creation of detailed, strand-based hair geometries without the need for 3D hair training data. This novel text-driven approach leverages existing 2D generative diffusion models to distill a realistic 3D representation, surpassing conventional avatar generation methods that inadequately model the complexity of hair due to generalized or entangled representations.

Key Contributions

1. Strand-Disentangled Hair Generation: StrandHead effectively separates hair and head generation, uniquely achieving strand-level hair modeling from pre-trained 2D models. This distinct ability stands out as it provides strand-based hair representation, allowing for realistic hairstyle variation and seamless integration into virtual environments.
2. Differentiable Prismatization Algorithm: Central to StrandHead's methodology is the introduction of a differentiable prismatization technique, transforming hair strands into watertight prismatic meshes. This innovation facilitates the use of mesh-based renderers in deep learning tasks, extending the framework's applicability by enabling accurate, detailed hair modeling.
3. Incorporation of Hair Priors: By integrating orientation consistency and curvature regularization losses derived from statistical analysis of hair geometry, the framework ensures realistic local and global hair shapes. These priors allow StrandHead to maintain coherence in hair orientation and match hair curvature to desired styles, effectively guiding the generation process.

Methodology

The StrandHead pipeline is divided into two main phases: generating a 3D bald head and then adding strand-based hair. Initial bald head creation relies on the DMTet representation, with optimization mediated by SDS loss and human-specialized diffusion models for improved detail and realism. For hair modeling, the distinct differentiable prismatization transforms strands for mesh processing, supported by orientation and curvature regularization to guide accurate, stylistically appropriate hair generation.

Evaluation and Results

The paper reports extensive experiments demonstrating StrandHead's superiority in generating head avatars with high fidelity facial details and complex hair texture. These results are validated against state-of-the-art (SOTA) methods, showcasing the framework's enhanced ability to produce realistic, diverse hairstyles that integrate seamlessly with technologies such as Unreal Engine for applications in physics-based rendering and simulation.

Implications and Future Directions

StrandHead's capabilities resonate well with demands in industries like digital telepresence, AR/VR, gaming, and film, where high-quality 3D avatars are crucial. The disentangled modeling also supports practical applications like hairstyle editing and transfer. The framework could be further expanded to integrate dynamic hairstyle modifications directly from textual input or enhance real-time performance for interactive applications.

Future developments may focus on extending the text-to-3D capabilities towards full-body avatar generation or incorporating more complex environmental interactions, potentially transforming how digital content is created and customized across various platforms. Continued research could explore broader generalization strategies, leveraging more advanced learning paradigms to reduce dependence on specific model pretraining.