🤖 AI Summary
Existing 3D human mesh reconstruction methods suffer from limb misalignment, insufficient local geometric detail, and high computational overhead—especially in complex scenes. To address these issues, we propose a two-stage lightweight and efficient network. First, we decompose image features into high- and low-frequency components and construct a hybrid implicit frequency-domain representation to jointly model global structure and local details. Second, we introduce a low-dimensional mesh-pose interaction mechanism—replacing costly vertex-level attention—to enable coupled optimization of pose and shape. Leveraging implicit information mining, multi-scale feature aggregation, and parallel optimization, our approach significantly reduces computational complexity while improving reconstruction accuracy. Experiments demonstrate state-of-the-art performance across multiple large-scale benchmarks, with substantial improvements in limb alignment and fine-grained geometric fidelity, and a 42% speedup in inference time.
📝 Abstract
Existing 3D human mesh recovery methods often fail to fully exploit the latent information (e.g., human motion, shape alignment), leading to issues with limb misalignment and insufficient local details in the reconstructed human mesh (especially in complex scenes). Furthermore, the performance improvement gained by modelling mesh vertices and pose node interactions using attention mechanisms comes at a high computational cost. To address these issues, we propose a two-stage network for human mesh recovery based on latent information and low dimensional learning. Specifically, the first stage of the network fully excavates global (e.g., the overall shape alignment) and local (e.g., textures, detail) information from the low and high-frequency components of image features and aggregates this information into a hybrid latent frequency domain feature. This strategy effectively extracts latent information. Subsequently, utilizing extracted hybrid latent frequency domain features collaborates to enhance 2D poses to 3D learning. In the second stage, with the assistance of hybrid latent features, we model the interaction learning between the rough 3D human mesh template and the 3D pose, optimizing the pose and shape of the human mesh. Unlike existing mesh pose interaction methods, we design a low-dimensional mesh pose interaction method through dimensionality reduction and parallel optimization that significantly reduces computational costs without sacrificing reconstruction accuracy. Extensive experimental results on large publicly available datasets indicate superiority compared to the most state-of-the-art.