Recently, a groundbreaking innovation in 3D human body reconstruction called LHM (Large-scale Human body Model) has emerged, charting a new course and promising exciting applications for the field.
Creating animatable 3D human body reconstructions from a single image has been a highly challenging task, plagued by ambiguities in geometry, appearance, and deformation separation. Current advancements mostly focus on static human modeling, often relying on synthetic 3D scans for training, which significantly limits their generalization capabilities in real-world scenarios. Meanwhile, optimization-based video methods require strict capture conditions and are computationally intensive, hindering practical applications.
To address these challenges, the LHM model was developed. This model innovatively employs a multi-modal transformer architecture, leveraging a powerful attention mechanism to effectively encode human pose features and image features. This architecture allows LHM to accurately reconstruct human body geometry while also preserving detailed clothing geometry and texture, resulting in more realistic and refined 3D human models.
It is worth noting that LHM also introduces a head feature pyramid encoding scheme. This scheme aggregates multi-scale features from the head region, further enhancing the model's ability to capture fine details of the human head, resulting in more realistic head representations in the generated 3D human models. In practical applications, LHM demonstrates remarkable efficiency, generating reasonably animated human bodies within seconds without requiring complex post-processing, significantly saving time and labor costs.
Extensive experimental validation shows that LHM outperforms existing methods in both reconstruction accuracy and generalization ability. Whether in complex scenes or under varying lighting conditions, LHM consistently outputs high-quality 3D human body reconstruction results.