Modulating Depth Map Features to Estimate 3D Human Pose via Multi-Task Variational Autoencoders

Sammanfattning: Human pose estimation (HPE) constitutes a fundamental problem within the domain of computer vision, finding applications in diverse fields like motion analysis and human-computer interaction. This paper introduces innovative methodologies aimed at enhancing the accuracy and robustness of 3D joint estimation. Through the integration of Variational Autoencoders (VAEs), pertinent information is extracted from depth maps, even in the presence of inevitable image-capturing inconsistencies. This concept is enhanced through the introduction of noise to the body or specific regions surrounding key joints. The deliberate introduction of noise to these areas enables the VAE to acquire a robust representation that captures authentic pose-related patterns. Moreover, the introduction of a localised mask as a constraint in the loss function ensures the model predominantly relies on pose-related cues while disregarding potential confounding factors that may hinder the compact representation of accurate human pose information. Delving into the latent space modulation further, a novel model architecture is devised, joining a VAE and fully connected network into a multi-task joint training objective. In this framework, the VAE and regressor harmoniously influence the latent representations for accurate joint detection and localisation. By combining the multi-task model with the loss function constraint, this study attains results that compete with state-of-the-art techniques. These findings underscore the significance of leveraging latent space modulation and customised loss functions to address challenging human poses. Additionally, these novel methodologies pave the way for future explorations and provide prospects for advancing HPE. Subsequent research endeavours may optimising these techniques, evaluating their performance across diverse datasets, and exploring potential extensions to unravel further insights and advancements in the field.