Recovering 3D Human Mesh from Monocular Images: A Survey (2203.01923v6)

Abstract: Estimating human pose and shape from monocular images is a long-standing problem in computer vision. Since the release of statistical body models, 3D human mesh recovery has been drawing broader attention. With the same goal of obtaining well-aligned and physically plausible mesh results, two paradigms have been developed to overcome challenges in the 2D-to-3D lifting process: i) an optimization-based paradigm, where different data terms and regularization terms are exploited as optimization objectives; and ii) a regression-based paradigm, where deep learning techniques are embraced to solve the problem in an end-to-end fashion. Meanwhile, continuous efforts are devoted to improving the quality of 3D mesh labels for a wide range of datasets. Though remarkable progress has been achieved in the past decade, the task is still challenging due to flexible body motions, diverse appearances, complex environments, and insufficient in-the-wild annotations. To the best of our knowledge, this is the first survey that focuses on the task of monocular 3D human mesh recovery. We start with the introduction of body models and then elaborate recovery frameworks and training objectives by providing in-depth analyses of their strengths and weaknesses. We also summarize datasets, evaluation metrics, and benchmark results. Open issues and future directions are discussed in the end, hoping to motivate researchers and facilitate their research in this area. A regularly updated project page can be found at https://github.com/tinatiansjz/hmr-survey.

• The paper reviews optimization-based and regression-based techniques for recovering 3D human mesh from monocular images.
• It details challenges like occlusions, complex pose variations, and sensitivity to initialization while using key evaluation metrics such as MPJPE and PA-MPJPE.
• The survey outlines implications for VR, AR, and motion capture, and suggests unified frameworks to advance 3D human modeling.

Estimating 3D Human Meshes from Monocular Images

The task of recovering 3D human mesh representations from monocular images is a persistent challenge within the field of computer vision. This paper by Tian et al. provides a comprehensive survey of methodologies employed to estimate human pose and shape using 3D mesh representations from single-camera input. The paper delineates two key paradigms: optimization-based techniques and regression-based approaches, each offering distinct methodologies for addressing the inherent challenges of transforming 2D observations into 3D reconstructions.

Paradigms and Methodologies

Optimization-Based Techniques: These methods aim to fit a predefined parametric model to detected 2D human features. They utilize data terms to measure alignment accuracy and regularization terms to ensure plausible body configurations. Such approaches often integrate 2D keypoint data, silhouettes, and segmentation information as constraints. Although these techniques achieve well-aligned 3D pose estimates, they are often complex, computationally expensive, and sensitive to initialization.

Regression-Based Approaches: Leveraging advances in deep learning, these methods seek to directly infer 3D model parameters via neural networks. These techniques have evolved to incorporate intermediate representations like 2D heatmaps and UV position maps to simplify the estimation process. The end-to-end pipeline in regression-based methods allows for the direct prediction of SMPL parameters, voxel representations, or mesh vertices, leading to flexible implementations capable of handling complex variations in human appearance and posture.

Challenges and Considerations

The survey addresses several underlying challenges in achieving accurate 3D reconstructions. These include handling occlusions, complex motion patterns, and integrating diverse human appearances—enhanced by parametric models like SMPL and SMPL-X. Ensuring physical plausibility remains a significant concern, with contact constraints and prior knowledge playing a critical role in curbing unrealistic body configurations.

Furthermore, the survey underscores the importance of datasets and evaluation metrics that facilitate benchmarking. Datasets such as Human3.6M and 3DPW provide essential ground truth annotations, while metrics like MPJPE and PA-MPJPE enable comprehensive evaluations of model performance.

Implications and Future Directions

This work embodies significant implications in the realms of virtual reality, augmented reality, and motion capture technologies, where precise human modeling is crucial. Practically, refinement of these techniques can lead to more immersive virtual experiences and enhance human-computer interaction interfaces. Theoretically, the exploration of human body representations in 3D space can drive further advancements in machine perception and human geometry understanding.

Looking to the future, the field may evolve towards more unified frameworks that efficiently integrate body, hand, and facial modeling comprehensively. Robust handling of occlusions and temporal coherence in video sequences remains an open challenge, ripe for exploration. Addressing these aspects will significantly enhance the capacity to model dynamic and interactive human forms within complex environments.