HeadArtist-VL: Vision / Language Guided 3D Head Generation with Self Score Distillation
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View abstract on PubMed
Summary
This summary is machine-generated.HeadArtist-VL generates high-quality 3D heads from vision or language inputs using self-score distillation (SSD). This method significantly outperforms existing techniques for 3D head generation and editing.
Area Of Science
- Computer Vision
- Computer Graphics
- Artificial Intelligence
Background
- Generating realistic 3D head models from diverse inputs remains a challenge.
- Existing methods often lack flexibility in handling both visual and textual guidance.
- Controlling the generation process with fine-grained details is crucial for applications.
Purpose Of The Study
- To introduce HeadArtist-VL, an efficient pipeline for 3D head generation and editing.
- To enable 3D head synthesis guided by either reference images or language prompts.
- To achieve high-fidelity 3D head models with controllable geometry and appearance.
Main Methods
- Utilizes a landmark-guided ControlNet as a generative prior for optimizing a 3D head model.
- Employs self-score distillation (SSD) for efficient pipeline optimization.
- Integrates vision and language inputs through a novel diffusion-based approach, including image encoders and novel-view synthesis.
Main Results
- HeadArtist-VL produces high-quality 3D head sculptures with rich geometry and photorealistic appearance.
- The method significantly outperforms state-of-the-art approaches in 3D head generation.
- Demonstrates successful editing operations, including geometry deformation and appearance modification.
Conclusions
- HeadArtist-VL offers a versatile and effective solution for 3D head generation and editing.
- The proposed self-score distillation (SSD) framework enables robust optimization under vision/language guidance.
- The method shows strong potential for applications requiring realistic and editable 3D head models.
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