The Pose Knows: Video Forecasting by Generating Pose Futures (1705.00053v1)

Abstract: Current approaches in video forecasting attempt to generate videos directly in pixel space using Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). However, since these approaches try to model all the structure and scene dynamics at once, in unconstrained settings they often generate uninterpretable results. Our insight is to model the forecasting problem at a higher level of abstraction. Specifically, we exploit human pose detectors as a free source of supervision and break the video forecasting problem into two discrete steps. First we explicitly model the high level structure of active objects in the scene---humans---and use a VAE to model the possible future movements of humans in the pose space. We then use the future poses generated as conditional information to a GAN to predict the future frames of the video in pixel space. By using the structured space of pose as an intermediate representation, we sidestep the problems that GANs have in generating video pixels directly. We show through quantitative and qualitative evaluation that our method outperforms state-of-the-art methods for video prediction.

• The paper presents a novel method that first predicts future human poses with a VAE before generating video frames via a GAN.
• It decouples high-level motion dynamics from pixel detail, achieving lower pose prediction errors and higher Inception scores on UCF-101.
• The approach offers a scalable framework with potential applications in real-time human-computer interaction and anomaly detection.

Overview of "The Pose Knows: Video Forecasting by Generating Pose Futures"

This paper tackles the challenging problem of video forecasting, specifically in predicting human motion over short future time spans. Unlike traditional methods that attempt direct pixel prediction in video space using Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), this work proposes a novel two-step approach to model forecasting at a higher abstraction level. By leveraging human pose detectors as an intermediate representation, the authors propose a method that first predicts human poses for future frames, followed by using these predictions to generate pixel-level future video frames.

Methodology

The methodology bifurcates the video forecasting task into two primary stages:

1. Pose Prediction with Pose-VAE: In the first phase, the focus is on predicting the future human poses using a Variational Autoencoder (VAE). This involves learning a distribution over possible future pose sequences, given a few prior frames. The model used here combines sequential encoder-decoder networks with a conditional VAE. By treating pose prediction as a probabilistic problem, the approach accounts for the inherent uncertainty in forecasting future movements.
2. Video Generation with Pose-GAN: The second phase takes the predicted pose sequences and generates the corresponding video frames using a Generative Adversarial Network (GAN). The GAN is conditioned on both the input image frame and the pose sequence, allowing it to focus on the challenge of pixel prediction while utilizing structured pose as guidance. This division ensures that the complexities of structure, dynamics, and pixel-level details are tackled separately, allowing for more accurate and interpretable outcomes.

Experimental Results

The authors provide a thorough evaluation on the UCF-101 dataset, demonstrating that their approach outperforms state-of-the-art methods in both pose prediction and video forecasting. The experiments in pose space show lower Euclidean error than baseline models, validating the effectiveness of the Pose-VAE in capturing diverse future human movements. In video space, the generated frames achieve higher Inception scores compared to previous works, indicating more realistic and coherent output.

Implications and Future Directions

The implications of this paper are significant for advancing real-time video forecasting applications including human-computer interaction and anomaly detection. By successfully decoupling human pose prediction from pixel-level video generation, this framework presents a scalable method capable of being trained on large, unlabeled datasets. The structured space of pose simplifies the prediction task, offering a potential pretext task for representation learning in video understanding.

In future research, integrating this approach within more complex structured RNNs may enhance pose modeling capabilities, while its application in unsupervised action recognition could uncover new dimensions of understanding in video data. Furthermore, exploring additional intermediate representations and leveraging advancements in pose estimation technologies can expand the model's utility across broader contexts in computer vision.