Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors (2105.01859v2)

Abstract: Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a human volumetric capture method that combines temporal volumetric fusion and deep implicit functions. To achieve high-quality and temporal-continuous reconstruction, we propose dynamic sliding fusion to fuse neighboring depth observations together with topology consistency. Moreover, for detailed and complete surface generation, we propose detail-preserving deep implicit functions for RGBD input which can not only preserve the geometric details on the depth inputs but also generate more plausible texturing results. Results and experiments show that our method outperforms existing methods in terms of view sparsity, generalization capacity, reconstruction quality, and run-time efficiency.

• The paper introduces a novel Dynamic Sliding Fusion technique that enforces temporal consistency and reduces noise in volumetric capture.
• It leverages detail-preserving deep implicit functions to enhance geometry with fine details and plausible textures.
• Evaluations against state-of-the-art methods demonstrated superior reconstruction accuracy under rapid motions and complex interactions.

Function4D: Real-time Human Volumetric Capture from Consumer RGBD Sensors

The paper "Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors" addresses the longstanding challenge of real-time human volumetric capture in computer vision and computer graphics. The primary novelty of this research lies in its utilization of very sparse consumer RGBD sensors to achieve high-quality volumetric capture. This capability poses a significant advantage over existing systems that require more complex hardware setups, including an extensive array of cameras and custom high-quality sensors, which are impractical for consumer-level applications.

Methodology

The authors present a hybrid approach that combines temporal volumetric fusion and deep implicit functions. The system, Function4D, effectively handles complex scenarios such as human-object interactions, clothing changes, and multi-person interactions, which remain challenging in real-time human volumetric capture. Key contributions include:

1. Dynamic Sliding Fusion (DSF): This novel technique improves upon traditional volumetric fusion methods by enforcing temporal consistency and noise reduction through the fusion of depth observations within a sliding window. DSF ensures topology consistency and minimizes tracking dependency, which is crucial for handling non-rigid tracking challenges.
2. Detail-preserving Deep Implicit Functions: These functions enhance the geometrics reconstructed from RGBD input by preserving fine geometric details and generating more plausible textures. Utilizing truncated PSDF values and integrating an attention mechanism in the multi-view feature aggregation stage are critical advancements that contribute to high-resolution and detailed surface reconstruction.
3. Dataset and Training: A curated set of 500 high-resolution scans with diverse poses and clothing options enables comprehensive training and evaluation, facilitating robust function and model performance validation.

Results and Evaluation

Function4D is evaluated against state-of-the-art methods like Motion2Fusion and Multi-view PIFu. It demonstrates superior performance under extreme conditions, including rapid motion changes and complex topology modifications. The detail-preserving capabilities and temporal consistency of Function4D were effectively highlighted in scenarios featuring intricate deformations and interactions, resulting in more precise and temporally coherent reconstructions.

Quantitatively, Function4D shows significant improvements in accuracy metrics such as P2S, Chamfer distances, and normal consistency, validating its efficacy over models that heavily rely on template-based approaches or multi-scale 3D convolution operations.

Implications and Future Directions

The proposed Function4D system represents a step forward in making real-time volumetric capture more accessible. By balancing the trade-off between hardware complexity and capture quality, it opens doors for applications in online education, gaming, and telepresence for consumer-level systems. The potential for integrating RGB information to improve geometry reconstruction, especially for difficult-to-capture materials, remains an open area for future research. Additionally, leveraging temporal observations more effectively in implicit functions could further enhance reconstruction details and functionality for occluded regions.

Conclusion

"Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors" stands as a significant contribution to reducing the resource barriers associated with real-time volumetric capture while enhancing the capture quality. Its innovative methodologies, especially Dynamic Sliding Fusion and detail-preserving deep implicit functions, provide a solid foundation for further developments in AI-driven human modeling and digital interaction environments. As the technology matures, improvements in geometric and texture accuracies can be expected, pushing the boundaries of real-time human volumetric capture.