3D Hand Shape and Pose from Images in the Wild (1902.03451v1)

Abstract: We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild. Our network consists of the concatenation of a deep convolutional encoder, and a fixed model-based decoder. Given an input image, and optionally 2D joint detections obtained from an independent CNN, the encoder predicts a set of hand and view parameters. The decoder has two components: A pre-computed articulated mesh deformation hand model that generates a 3D mesh from the hand parameters, and a re-projection module controlled by the view parameters that projects the generated hand into the image domain. We show that using the shape and pose prior knowledge encoded in the hand model within a deep learning framework yields state-of-the-art performance in 3D pose prediction from images on standard benchmarks, and produces geometrically valid and plausible 3D reconstructions. Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunction with full supervision in the form of 3D joint annotations on limited available datasets allows for good generalization to 3D shape and pose predictions on images in the wild.

• The paper introduces an end-to-end deep learning approach combining a convolutional encoder with a model-based decoder for accurate 3D hand reconstruction.
• It employs a dual training paradigm using weak 2D and full 3D supervision to generalize effectively on complex, real-world images.
• Quantitative evaluations on datasets like Mpii+Nzsl show that the method significantly outperforms existing techniques in 3D hand pose estimation.

Analysis of "3D Hand Shape and Pose from Images in the Wild"

The paper entitled "3D Hand Shape and Pose from Images in the Wild" by Boukhayma et al. addresses the challenging problem of hand pose and shape estimation from monocular RGB images. This task holds considerable applications in sectors such as augmented reality and human-computer interaction, marking its relevance across multiple domains.

The authors introduce an end-to-end deep learning approach that combines both model-based methods and learning-based techniques. The proposed architecture integrates a deep convolutional encoder and a fixed model-based decoder to predict hand shape and pose parameters from input images. This system, unique in its design, leverages the advantages of both methodologies, offering state-of-the-art performance in 3D hand pose estimation benchmarks without the need for post-processing optimizations.

Architecture Overview

The network architecture comprises a convolutional encoder and a model-based decoder. The encoder processes the RGB input image to output parameters controlling hand shape and pose, as well as view parameters. The fixed decoder uses these parameters to construct a 3D hand mesh, leveraging a differentiable mesh deformation model inspired by MANO. A critical component is the re-projection module, which converts the 3D hand representation back to the 2D image plane using a weak perspective camera approach. This synergy allows the method to predict plausible and geometrically valid hand poses, bridging the gap between 2D observations and 3D reconstructions.

Innovative Contributions

Boukhayma et al. advance the field with two primary contributions:

1. Training Paradigm: They show convincing results in training the network with a combination of weak supervision using only 2D annotations from large-scale datasets and full supervision from 3D annotations on smaller sets. This dual approach enables effective generalization to intricate real-world data, overcoming the limitations of scarce annotated 3D hand datasets.
2. Model-Driven Decoder: The paper emphasizes the impact of incorporating a generative model of the hand, facilitating the prediction of realistic hand poses and shapes. The use of a linear blend skinning model ensures that the hand configurations produced are within the field of physical feasibility.

Evaluation and Results

The authors present quantitative and qualitative evaluations across several public datasets, demonstrating notable advancements over existing approaches. Particularly, the model displays robust performance on challenging datasets such as Mpii+Nzsl, which consists of images with various complexities including occlusions and motion blur.

The proposed method achieves superior 3D pose estimation accuracy compared to both deep learning-based methodologies and non-deep learning competitors. Specifically, the model shows a substantial improvement in real-world conditions, as evidenced by the numerical results drawn from benchmark evaluations.

Implications and Future Directions

This research advances the understanding and application of 3D hand pose estimation in uncontrolled settings. Its practical implications span interactive applications, enabling more intuitive and accurate hand monitoring in consumer electronics and virtual environments.

Future work might explore extensions such as integrating an appearance model, potentially refining the fine-grained accuracy of the reconstructive process. Additionally, unlocking greater flexibility in the decoder, potentially through end-to-end trainable corrective blend shapes, could push the boundary further in capturing dynamic hand motions with increased realism.

Overall, Boukhayma et al. provide a significant step forward in leveraging RGB images for hand pose and shape reconstruction, offering promising directions for both theoretical exploration and practical deployment in real-world applications.