Human Interaction-Aware 3D Reconstruction from a Single Image
Summary: arXiv:2604.05436v1 Announce Type: cross
Reconstructing textured 3D human models from a single image is fundamental for augmented reality (AR), virtual reality (VR), and digital human applications. Traditional methods primarily focus on single individuals, which poses challenges when dealing with multi-human scenes. The naive composition of individual reconstructions often results in artifacts such as unrealistic overlaps, missing geometry in occluded regions, and distorted interactions. These limitations underline the necessity for approaches that incorporate both group-level context and interaction priors.
Introducing HUG3D
To address these challenges, researchers have introduced a holistic method that explicitly models both group-level and instance-level information. The framework, termed Human Group-Instance Multi-View Diffusion (HUG-MVD), effectively generates complete multi-view normals and images by jointly modeling individuals and group context. This innovative approach resolves issues related to occlusions and proximity, which are critical in multi-human scenes.
Key Components of HUG3D
- Canonical Orthographic Space Transformation: To mitigate perspective-induced geometric distortions, the input image is transformed into a canonical orthographic space. This transformation serves as a foundation for subsequent processing.
- Human Group-Instance Multi-View Diffusion (HUG-MVD): This primary component generates comprehensive multi-view normals and images by integrating individual and group context. This integration is crucial for accurately representing occlusions and interactions among multiple individuals.
- Human Group-Instance Geometric Reconstruction (HUG-GR): This module optimizes the geometry by leveraging explicit, physics-based interaction priors. By enforcing physical plausibility, it accurately models inter-human contact, which is essential for realistic 3D reconstructions.
- High-Fidelity Texture Fusion: The multi-view images produced are then fused to create a high-fidelity texture, enhancing the visual quality of the final 3D model.
Performance and Results
Extensive experiments conducted by the research team demonstrate that HUG3D significantly outperforms both single-human and existing multi-human methods. The framework is capable of producing physically plausible, high-fidelity 3D reconstructions of interacting people from just a single image. This advancement has substantial implications for various applications, including gaming, film production, and virtual social interactions.
Conclusion
The introduction of HUG3D marks a significant leap forward in the field of 3D reconstruction, particularly in scenarios involving multiple interacting individuals. By addressing the challenges posed by traditional methods, HUG3D not only enhances the quality of 3D human models but also paves the way for more immersive and realistic AR/VR experiences. For more details, visit the HUG3D project page.