Monocular Expressive Body Regression through Body-Driven Attention (2008.09062v1)

Abstract: To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .

• The paper presents ExPose, a method that directly regresses SMPL-X parameters to capture detailed 3D human pose and shape from monocular images.
• It employs a body-driven attention mechanism to refine face and hand regions, addressing resolution challenges in expressive modeling.
• The approach outperforms state-of-the-art methods with two orders of magnitude lower computation while maintaining or improving accuracy.

Monocular Expressive Body Regression

The paper "Monocular Expressive Body Regression" presents a novel approach, ExPose (EXpressive POse and Shape rEgression), for accurately and efficiently capturing the full 3D human body, including the face and hands, from a monocular RGB image. This task fundamentally contributes to the progression of comprehensive human understanding in computer vision, addressing both the high dimensionality and the scarcity of expressive training datasets that have traditionally made this problem challenging.

Key Contributions

The paper tackles several limitations of existing methods, which mainly focus on isolated body parts or are optimization-based, suffering from slow computation and proneness to local optima. ExPose advances beyond these by directly regressing the SMPL-X parameters—an expressive 3D body model that integrates body, face, and hands—thereby enhancing computational efficiency without sacrificing accuracy.

The authors make three significant contributions:

1. Curated Dataset: They overcome the lack of expressive training data by curating a dataset of SMPL-X fits on in-the-wild images. This approach is resourceful in leveraging existing datasets while curtailing the labor-intensive process of acquiring new, annotated data.
2. Body-driven Attention: ExPose introduces attention mechanisms driven by body estimation to localize and refine face and hand regions with higher-resolution images, compensating for the typical loss of detail in these smaller and critical regions when reduced to a neural network's input size.
3. Part-specific Knowledge Exploitation: Dedicated refinement modules for face and hand use specialized insights gained from face-only and hand-only datasets, ensuring high fidelity in their detailed reconstructions despite the sparse data.

Results and Implications

ExPose is evaluated against state-of-the-art methods across various benchmarks. It outperforms existing models like SMPLify-X in terms of computational efficiency, achieving similar or superior accuracy at a fraction of the computational cost. Notably, ExPose is found to be faster by two orders of magnitude compared to optimization-based methods, a critical advantage for applications requiring real-time processing.

The implications of this research are significant for practical and theoretical domains within AI and computer vision. Practically, the ability to swiftly and accurately understand human pose, gesture, and expression from a single image carries potential for vast application in human-computer interaction, entertainment, surveillance, and medical diagnostics. Theoretically, this paper's approach sets a new standard for utilizing high-dimensional data in neural networks, potentially influencing future models that aim to integrate complex, multi-faceted input into succinct, actionable output.

Future Directions

The authors suggest extending ExPose for multi-human interaction contexts and temporal sequences to cater to video data, thereby amplifying its applicability across dynamic and populated environments. Furthermore, there is an intent to refine the model's capability in deducing body shape and achieving pixel-level alignment, improving the nuanced understanding of interactions within scenes—be they between humans or between humans and objects or environments.

In conclusion, "Monocular Expressive Body Regression" represents a tangible advancement in expressive human modeling, with ExPose embodying an efficient, integrative approach that aligns with the growing demands for quick and detailed human understanding in diverse computational applications.