Unlike previous methods relying heavily on synthetic 3D scans or computationally intensive optimization, LHM is trained on large-scale video datasets with an image reconstruction loss, which boosts its generalization ability to diverse real-world scenarios. The feed-forward nature of the model enables fast inference, making it practical for applications such as virtual reality, gaming, e-commerce, and entertainment where quick generation of detailed and animatable human avatars is essential. The model also supports outputting 3D mesh files in formats like OBJ for further editing and integration into downstream workflows. Its integrated Gradio interface allows local visualization and interactive pose manipulation, enhancing user experience and flexibility.
Extensive experiments demonstrate that LHM outperforms existing state-of-the-art methods in reconstruction accuracy, animation consistency, and generalization to unseen poses and appearances. Its architecture effectively combines 3D positional encoding and 2D image features, enabling joint reasoning across geometric and visual domains. Although current datasets have some limitations in pose diversity and viewpoint coverage, ongoing efforts aim to improve training strategies and dataset curation to enhance robustness further. Overall, LHM represents a significant advancement in single-image 3D human reconstruction, offering a powerful, efficient, and accessible tool for generating realistic and animatable digital humans.
Key features include:
- Multimodal transformer architecture encoding body positional and image features with attention
- 3D Gaussian splatting representation for high-fidelity avatar reconstruction
- Head feature pyramid encoding for enhanced face identity and fine detail preservation
- Real-time animatable 3D human model generation from a single image without post-processing
- Supports output of editable 3D mesh files (e.g., OBJ format)
- Trained on large-scale video datasets for strong generalization to real-world scenarios
- Integrated interface for visualization and pose adjustment