Disambiguating Monocular Reconstruction of 3D Clothed Human with Spatial-Temporal Transformer

Reconstructing 3D clothed humans from monocular camera data is highly challenging due to viewpoint limitations and image ambiguity. While implicit function-based approaches, combined with prior knowledge from parametric models, have made significant progress, there are still two notable problems. Firstly, the back details of human models are ambiguous due to viewpoint invisibility. The quality of the back details depends on the back normal map predicted by a convolutional neural network (CNN). However, the CNN lacks global information awareness for comprehending the back texture, resulting in excessively smooth back details. Secondly, a single image suffers from local ambiguity due to lighting conditions and body movement. However, implicit functions are highly sensitive to pixel variations in ambiguous regions. To address these ambiguities, we propose the Spatial-Temporal Transformer (STT) network for 3D clothed human reconstruction. A spatial transformer is employed to extract global information for normal map prediction. The establishment of global correlations facilitates the network in comprehending the holistic texture and shape of the human body. Simultaneously, to compensate for local ambiguity in images, a temporal transformer is utilized to extract temporal features from adjacent frames. The incorporation of temporal features can enhance the accuracy of input features in implicit networks. Furthermore, to obtain more accurate temporal features, joint tokens are employed to establish local correspondences between frames. Experimental results on the Adobe and MonoPerfCap datasets have shown that our method outperforms state-of-the-art methods and maintains robust generalization even under low-light outdoor conditions.

An Embeddable Implicit IUVD Representation for Part-based 3D Human Surface Reconstruction

To reconstruct a 3D human surface from a single image, it is important to consider human pose, shape and clothing details simultaneously. In recent years, a combination of parametric body models (such as SMPL) that capture body pose and shape prior, and neural implicit functions that learn flexible clothing details, has been used to integrate the advantages of both approaches. However, the combined representation introduces additional computation, e.g. signed distance calculation, in 3D body feature extraction, which exacerbates the redundancy of the implicit query-and-infer process and fails to preserve the underlying body shape prior. To address these issues, we propose a novel IUVD-Feedback representation, which consists of an IUVD occupancy function and a feedback query algorithm. With this representation, the time-consuming signed distance calculation is replaced by a simple linear transformation in the IUVD space, leveraging the SMPL UV maps. Additionally, the redundant query points in the query-and-infer process are reduced through a feedback mechanism. This leads to more reasonable 3D body features and more effective query points, successfully preserving the parametric body prior. Moreover, the IUVD-Feedback representation can be embedded into any existing implicit human reconstruction pipelines without modifying the trained neural networks. Experiments on THuman2.0 dataset demonstrate that the proposed IUVD-Feedback representation improves result robustness and achieves three times faster acceleration in the query-and-infer process. Furthermore, this representation has the potential to be used in generative applications by leveraging its inherited semantic information from the parametric body model.