SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction

Abstract: Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements, accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images, along with predicting textures for unseen areas, remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response, we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction), a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline.SIFU employs a cross-attention mechanism within the transformer, using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness, especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments, SIFU surpasses SOTA methods in both geometry and texture reconstruction, showcasing enhanced robustness in complex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building, demonstrating its broad utility in real-world scenarios. Project page https://river-zhang.github.io/SIFU-projectpage/ .

• The paper introduces SIFU, which employs a side-view decoupling transformer to enhance the mapping from 2D images to precise 3D models.
• The paper implements a 3D consistent texture refinement pipeline leveraging diffusion models to synthesize realistic textures for both visible and unseen areas.
• The paper achieves state-of-the-art performance with low Chamfer distances and improved PSNR and LPIPS scores, highlighting its practical utility in AR/VR and digital content creation.

Overview of "SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction"

The paper presents a sophisticated approach for reconstructing high-quality 3D models of clothed humans from single images, addressing notable challenges within the domain, such as handling complex poses and realistic texture generation for unseen areas. The methodology, termed "SIFU" (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction), integrates a novel combination of a side-view decoupling transformer and a 3D consistent texture refinement pipeline.

Main Contributions

1. Side-view Conditioned Implicit Function:
   • The core innovation lies in the employment of a side-view decoupling transformer. This component utilizes cross-attention mechanisms enriched with SMPL-X normals to decouple features in side views and enhance the precision of feature mapping from 2D to 3D. This improves the robustness and accuracy of the geometric reconstruction process, particularly where SMPL-X estimates may be deficient.
2. 3D Consistent Texture Refinement:
   • The framework incorporates a sophisticated texture refinement technique. It leverages text-to-image diffusion models, adeptly generating consistent, high-quality textures across different views. This process ensures not only the synthesis of realistic textures in visible areas but also in regions hidden from the initial view, maintaining style and coherence throughout the model.

Experimental Results

The methodology demonstrates its efficacy through extensive evaluations, notably surpassing state-of-the-art (SOTA) methods in both geometry and texture quality metrics. Quantitatively, SIFU achieves remarkable Chamfer and P2S measurements, with values as low as 0.6 cm on the THuman2.0 dataset, highlighting its superior capability in accurate 3D reconstructions across diverse scenarios.

In robustness evaluation, SIFU exhibits substantial resilience to inaccuracies in SMPL-X estimations, further cementing its practical utility. Additionally, texture quality assessments reveal marked improvements over traditional methods, as illustrated by higher PSNR and lower LPIPS scores, emphasizing the enhanced visual fidelity achieved by SIFU.

Implications and Future Directions

The introduction of SIFU significantly advances real-world applicable 3D human reconstruction, offering potential utility in varied sectors such as AR/VR, 3D printing, and digital content creation. The model’s adept handling of complex poses and loose clothing, alongside its refined texture generation capabilities, signifies a pivotal step towards more accessible and cost-effective 3D modeling solutions without the need for comprehensive multi-view setups or manual artistry.

Future research could explore the integration of advanced generative models to further refine both geometric and textural outputs, potentially enhancing application domains like animation and simulation. The possibility of leveraging diffusion models as priors in additional stages of the reconstruction process might also unlock new avenues for improving model fidelity and reducing computational overhead.

Conclusion

The paper's contributions through SIFU represent a substantial stride in 3D human modeling from monocular inputs. By intricately combining innovative feature extraction techniques with robust texture refinement, it addresses longstanding challenges in the field, presenting a comprehensive solution that aligns well with practical application needs. This work lays a foundation for further exploration and development in both the theoretical aspects of AI-driven 3D reconstruction and its practical implementations.