Self-Supervised Multi-Person Multi-View 3D Pose Estimation without Ground-Truth Poses

Core Concepts

A self-supervised approach, called SelfPose3d, is proposed to estimate 3D poses of multiple persons from multi-view images without using any 2D or 3D ground-truth poses. The approach utilizes synthetic 3D root points, differentiable cross-affine-view 2D joints and heatmap rendering, and an adaptive supervision attention mechanism to learn 3D poses in a self-supervised manner.

Abstract

The paper presents a self-supervised approach, called SelfPose3d, for estimating 3D poses of multiple persons from multi-view images. Unlike current fully-supervised methods that require 2D or 3D ground-truth poses, SelfPose3d only uses multi-view input images and pseudo 2D poses generated from an off-the-shelf 2D human pose estimator.
The key components of the approach are:

Self-supervised 3D root localization: A synthetic dataset of 3D root points and their corresponding multi-view root heatmaps is used to train a 3D root localization model. This model is further regularized using an affine consistency constraint.

Self-supervised 3D pose estimation: The 3D poses are modeled as a bottleneck representation. These 3D poses are projected to 2D joints in each view, which are then rendered into differentiable 2D heatmap representations. The model is trained to minimize the L1 loss between the projected 2D joints and the pseudo 2D poses, as well as the L2 loss between the rendered heatmaps and the pseudo 2D poses.

Adaptive supervision attention: To address the inaccuracies in the pseudo 2D poses, an adaptive supervision attention mechanism is proposed. For the L1 joint loss, a hard attention strategy is used to ignore the view with the largest error. For the L2 heatmap loss, a soft attention mechanism is employed using a lightweight backbone to generate attention heatmaps.

Extensive experiments on the Panoptic, Shelf, and Campus datasets show that SelfPose3d achieves performance comparable to fully-supervised approaches, while significantly outperforming optimization-based methods that do not use ground-truth poses. Qualitative results demonstrate the ability of SelfPose3d to handle occlusions and multiple persons, as well as the plausibility of the estimated 3D poses and body meshes.

Stats

The 3D root localization model is trained on a synthetic dataset of randomly placed 3D points and their corresponding multi-view root heatmaps.

Quotes

"We propose a novel self-supervised learning objective that aims to recover 2d joints and heatmaps under different affine transformations from the bottleneck 3d poses."
"To address the inaccuracies in the pseudo 2d poses, we propose an adaptive supervision attention mechanism."

Key Insights Distilled From

SelfPose3d

by Vinkle Sriva... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02041.pdf

Deeper Inquiries

How can the self-supervised approach be extended to handle more complex scenes, such as those with significant occlusions or interactions between multiple persons

To handle more complex scenes with significant occlusions or interactions between multiple persons, the self-supervised approach can be extended in several ways. One approach could involve incorporating temporal information from video sequences to improve the understanding of occluded poses and interactions. By leveraging the temporal consistency between frames, the model can infer occluded poses by predicting the missing parts based on the visible parts in adjacent frames. Additionally, introducing attention mechanisms that focus on relevant regions in the presence of occlusions can help the model prioritize information from less occluded areas. Another strategy could involve generating synthetic data with varying levels of occlusions and interactions to train the model to generalize better to complex scenes. By exposing the model to diverse scenarios during training, it can learn to handle occlusions and interactions more effectively in real-world settings.

What other self-supervised learning techniques could be explored to further improve the performance of the 3D pose estimation without ground-truth data

To further enhance the performance of 3D pose estimation without ground-truth data, exploring additional self-supervised learning techniques can be beneficial. One approach could involve leveraging self-supervised representation learning methods to extract more informative features from the input data. By training the model to learn meaningful representations of the input images, it can improve its ability to estimate 3D poses accurately. Another technique to explore is self-supervised task learning, where the model is trained on auxiliary tasks related to 3D pose estimation. For example, training the model to predict the relative poses between body parts or estimating the depth ordering of joints can provide valuable supervisory signals that enhance the model's understanding of 3D poses. Additionally, incorporating unsupervised domain adaptation techniques can help the model generalize better to unseen data distributions, improving its robustness and performance in diverse scenarios.

What are the potential applications of this self-supervised 3D pose estimation approach beyond human pose estimation, such as in robotics or augmented reality

The self-supervised 3D pose estimation approach has a wide range of potential applications beyond human pose estimation. In robotics, this technique can be utilized for robot perception and manipulation tasks, enabling robots to understand the 3D poses of objects and interact with the environment more effectively. For example, robots can use 3D pose estimation to grasp objects accurately, navigate complex environments, and interact with humans in a more intuitive manner. In augmented reality (AR), the self-supervised approach can be used for real-time pose estimation of objects and people in the AR environment, enhancing the user experience and enabling more immersive AR applications. By accurately estimating 3D poses in real-time, AR systems can overlay virtual objects seamlessly into the physical world, creating interactive and engaging experiences for users. Additionally, in fields like sports analytics, healthcare, and animation, self-supervised 3D pose estimation can be applied for motion analysis, patient monitoring, and character animation, respectively, opening up new possibilities for innovation and advancement.