Human Pose Forecasting via Deep Markov Models (1707.09240v2)

Abstract: Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To this end, our contributions are threefold: (i) we propose a generative framework for poses using variational autoencoders based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a pose-based action classifier, which we argue better reflects the subjective quality of pose forecasts than distance in coordinate space; (iii) last, for evaluation of the new model, we introduce a 480,000-frame video dataset called Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling and disassembling furniture. We demonstrate promising results for our approach on both Ikea FA and the existing NTU RGB+D dataset.

• The paper introduces a novel generative framework using Deep Markov Models (DMMs) and variational autoencoders to model the stochasticity in long-term human pose forecasting.
• A new benchmark dataset, Ikea Furniture Assembly (Ikea FA), is introduced, featuring 480,000 frames of repetitive human activity for evaluating long-sequence pose forecasting.
• Results show the DMM approach outperforms state-of-the-art models in handling long sequences by producing diverse yet plausible outcomes, while highlighting challenges in temporal consistency.

Human Pose Forecasting via Deep Markov Models: A Comprehensive Overview

The paper "Human Pose Forecasting via Deep Markov Models" emphasizes the challenges and methodologies associated with predicting human body poses over extended periods. It introduces a new approach that leverages Deep Markov Models (DMMs) coupled with variational autoencoders to address the inherent stochasticity and uncertainty in human motion forecasting. This paper is particularly relevant for applications in human-robot interaction, visual surveillance, and autonomous driving, where anticipating human actions based on pose sequences is critical.

Methodological Contributions

The authors propose a novel generative framework utilizing DMMs, which adeptly models the unpredictability in human motion. The DMM-based system is trained to sample multiple plausible future pose sequences from observed data, an advancement over deterministic models that yield only a single anticipated alteration. Notably, this method circumvents the common pitfall of deterministic approaches tending towards mean convergence—a phenomenon where predictions average out and lose fidelity to realistic motion patterns.

To complement the model's predictions, the authors employ an action classifier that assesses the sequences' realism. This classifier, based on recurrent neural networks (RNNs), offers a robust evaluative measure by transforming pose sequences into action labels and comparing them against ground truth labels. Such a method aligns more closely with human intuition about plausible motion continuation than traditional metrics like coordinate-based distance.

Dataset Contribution

A significant addition to the domain is the introduction of the Ikea Furniture Assembly (Ikea FA) dataset, a 480,000-frame collection specifically designed for pose forecasting evaluation. This dataset features repeated human activities involving furniture assembly and disassembly, offering a regularity and repetitiveness that existing datasets like NTU RGB+D lack. The Ikea FA dataset's utility lies in its ability to serve as a benchmark for long-sequence pose forecasting, paving the way for systematic evaluation in this nascent research area.

Results and Analysis

The DMM approach demonstrated notable improvement in handling long-sequence pose forecasting tasks, outperforming state-of-the-art models, including Encoder-Recurrent-Decoder (ERD) frameworks and multi-layered LSTM networks. One of the paper's pivotal insights is the recognition of the zero-velocity model's effectiveness in shorter time horizons, a simple yet effective baseline in several cases. This indicates room for refinement in predictive models, particularly in balancing short-term accuracy with long-term prediction stability.

The DMM showcased an ability to produce diverse yet plausible outcomes by effectively capturing the stochastic nature of human motion. However, it also highlighted the challenge of temporal inconsistency in predictions over extended sequences. This difficulty points towards areas for future research, such as enhancing continuity in sampled pose transitions without losing the capacity to model diverse motion paths.

Implications and Future Directions

The research implications are manifold. Practically, improved pose forecasting can enhance real-time responsiveness in robots and increase safety in autonomous driving by predicting potential human actions well in advance. Theoretically, the introduction of stochastically-informed sequence models opens new avenues for developing models that can learn from and adapt to the inherent randomness in human actions.

Future developments could explore more intricate architectures that incorporate not just visual contexts but also environmental interactions, potentially using pixel-level prediction models alongside pose forecasting to achieve more comprehensive anticipations. Moreover, addressing the subtle drift in pose predictions using alternative parametrizations or innovations in network architecture may yield further benefits.

This paper's methodological advances and dataset contributions significantly enhance the understanding and capability of AI systems in anticipating human motion, setting a foundational framework for subsequent research endeavors in human pose forecasting and related fields.