Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation
(2407.07532)Abstract
With the explosive growth of available training data, single-image 3D human modeling is ahead of a transition to a data-centric paradigm. A key to successfully exploiting data scale is to design flexible models that can be supervised from various heterogeneous data sources produced by different researchers or vendors. To this end, we propose a simple yet powerful paradigm for seamlessly unifying different human pose and shape-related tasks and datasets. Our formulation is centered on the ability - both at training and test time - to query any arbitrary point of the human volume, and obtain its estimated location in 3D. We achieve this by learning a continuous neural field of body point localizer functions, each of which is a differently parameterized 3D heatmap-based convolutional point localizer (detector). For generating parametric output, we propose an efficient post-processing step for fitting SMPL-family body models to nonparametric joint and vertex predictions. With this approach, we can naturally exploit differently annotated data sources including mesh, 2D/3D skeleton and dense pose, without having to convert between them, and thereby train large-scale 3D human mesh and skeleton estimation models that outperform the state-of-the-art on several public benchmarks including 3DPW, EMDB and SSP-3D by a considerable margin.
Overview
-
The authors introduce Neural Localizer Fields (NLF), a method that predicts any point within a continuous canonical human volume for 3D human pose and shape estimation from single RGB images, surpassing traditional methods limited to predefined joint sets.
-
The NLF framework seamlessly integrates multiple heterogeneous data sources without necessitating manual re-annotation, thus enhancing the data efficiency and flexibility of the model.
-
Experimental results demonstrate that NLF achieves superior performance on several benchmark datasets, emphasizing its capability in accurate body shape estimation and generalization across different types of annotations.
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation
The paper "Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation" introduces a novel approach to the challenging task of 3D human pose and shape estimation from single RGB images. The authors propose Neural Localizer Fields (NLF), a method that enables seamless integration of various data sources and annotation formats during both training and inference. This approach represents a significant departure from the traditional finite and fixed joint sets typically employed in human pose estimation models.
Key Contributions
- Continuous Localizer Fields: The core contribution of the paper is the introduction of Neural Localizer Fields, which can predict any point within a continuous canonical human volume, not limited to a predefined set of joints or keypoints. This is achieved by learning a continuous field of localizer functions through a neural field.
- Heterogeneous Data Integration: The NLF framework allows for the seamless integration of multiple heterogeneous data sources without requiring manual re-annotation to a common format. This is a marked improvement over traditional methods that often struggle with the diversity of annotations from different datasets.
- Efficient Parametric Fitting: The authors present an efficient post-processing step that fits SMPL-family body models to the nonparametric predictions generated by NLF. This algorithm is designed to be fast, completing in just a few iterations, and can leverage GPU acceleration for efficient computation.
Methodology
The proposed NLF method involves a point localizer network (PLN) that predicts 3D points from image features. The localizer field $\Psi$ maps any point ( p ) in the canonical human volume to a convolutional network output that predicts the location of ( p ) in 3D observation space. By modulating the network's convolutional layer dynamically, the method can estimate the location of any queried point both during training and inference.
To represent high-frequency signals needed for accurate pose estimation, NLF employs positional encodings derived from the global point signature (GPS) using the volumetric Laplacian. This encoding helps in capturing fine-grained geometric details essential for accurate prediction.
Experimental Results
The authors evaluate NLF extensively on several benchmark datasets, showcasing its superior performance compared to state-of-the-art methods. Key results include:
- EMDB: NLF achieves an MPJPE of 68.4 mm, significantly outperforming the second-best method, BEDLAM-CLIFF, which scores 98.0 mm.
- 3DPW: NLF displays strong performance with an MPJPE of 54.1 mm and further improvement to 53.2 mm with temporal smoothing.
- AGORA: NLF achieves state-of-the-art performance in SMPL-X prediction with an NMVE of 99.2 mm.
- SSP-3D: NLF yields a low PVE-T-SC of 10.0 mm, underscoring its capability in accurate body shape estimation.
- Human3.6M, MPI-INF-3DHP, and MuPoTS-3D: NLF performs exceptionally well on these skeleton estimation benchmarks, validating its generality across different types of annotations.
Implications and Future Directions
The introduction of Neural Localizer Fields presents a unifying approach that could potentially streamline future research in human pose and shape estimation. By enabling training on heterogeneous datasets without cumbersome re-annotation, NLF opens new opportunities for leveraging diverse data sources.
The proposed fitting algorithm further enhances the utility of NLF by providing a fast and efficient way to obtain parametric body model representations from nonparametric predictions, making it valuable for downstream applications.
Future research could explore extending NLF to handle more complex scenarios such as interactions between multiple humans or integration with temporal sequence data for improved motion understanding. Additionally, refining the uncertainty estimation could further enhance the reliability of the model in real-world applications.
In summary, the Neural Localizer Fields approach significantly advances the state-of-the-art in 3D human pose and shape estimation by introducing a flexible, data-efficient, and high-performing framework. The ability to handle continuous and arbitrary point predictions within the human body volume offers a robust solution adaptable to various real-world scenarios and diverse datasets.
Create an account to read this summary for free: