Long-term Human Motion Prediction with Scene Context (2007.03672v3)

Abstract: Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine how hard it is to navigate a new room with lights off. Existing works on predicting human motion do not pay attention to the scene context and thus struggle in long-term prediction. In this work, we propose a novel three-stage framework that exploits scene context to tackle this task. Given a single scene image and 2D pose histories, our method first samples multiple human motion goals, then plans 3D human paths towards each goal, and finally predicts 3D human pose sequences following each path. For stable training and rigorous evaluation, we contribute a diverse synthetic dataset with clean annotations. In both synthetic and real datasets, our method shows consistent quantitative and qualitative improvements over existing methods.

• The paper introduces a novel three-stage framework that predicts long-term human motion by determining 2D goals, planning 3D paths, and refining 3D poses using scene context.
• It employs a combination of conditional variational autoencoders, convolutional networks, and transformers to integrate environmental cues into motion forecasting.
• Evaluation on synthetic and real datasets shows significant accuracy improvements over traditional models, highlighting its potential in robotics and autonomous systems.

Long-term Human Motion Prediction with Scene Context

This paper introduces an innovative approach to predicting long-term human motion by leveraging scene context, a dimension often overlooked by prior works. The primary contribution lies in a three-stage framework that first determines potential human motion goals, then plans 3D paths, and finally predicts sequences of 3D poses. This comprehensive approach reflects a shift from previous models that were limited to short-term predictions and did not incorporate environmental cues.

Key Contributions

The framework is distinguished by its sensitivity to the spatial configurations of objects within a scene, aligning human motion prediction with realistic environmental interactions. The goal-oriented prediction model reasons humans' movement paths in relation to spatial obstacles and destinations, employing a variational approach to capture multiple plausible motion trajectories.

1. GoalNet: This module utilizes conditional variational autoencoders to predict potential 2D destinations, introducing multiple hypotheses to address the uncertainty in human movement goals. The predicted 2D destinations serve as the foundation for subsequent path and pose predictions.
2. PathNet: It translates 2D goals into 3D paths considering scene constraints, employing convolutional networks that learn from diverse human-scene interaction data. This stage ensures the predicted paths are in line with realistic spatial navigation, avoiding obstacles and respecting the scene layout.
3. PoseNet: This stage refines initial 3D pose estimates derived from 2D inputs and path predictions. It employs transformer networks to enhance the realism of pose sequences, particularly under the constraints imposed by the surrounding environment.

Dataset Contribution

To enable robust training and evaluation, the paper introduces the GTA Indoor Motion dataset. This synthetic dataset addresses the limitations of existing real datasets, such as noise and limited motion range, by providing over a million frames with high-quality annotations of human motion and scene interaction. The dataset is diverse, covering a wide array of scene types and human activities.

Evaluation and Findings

The proposed framework demonstrates significant improvements over baseline methods, in both synthetic (GTA-IM) and real (PROX) datasets. Results highlight its capability to manage longer-term predictions up to three seconds, outperforming traditional models that ignore scene context. The stochastic model specifically, which samples multiple possible futures, showcases enhanced prediction precision as the number of samples increases, suggesting its utility in probabilistic motion forecasting.

Implications and Future Directions

This research implies considerable advancements in fields like robotics, virtual reality, and autonomous vehicles, where understanding and forecasting human motion within environments are critical. The model's ability to predict plausible paths and poses respecting environmental constraints hints at its potential for improving human-computer interactions and enhancing machine perception in dynamic settings.

Future research could expand on integrating dynamic objects within scenes and involve multiple interacting agents, which could further bridge the gap between real-world complexities and predictive modeling in human motion. Moreover, refining prediction models to include temporal or multi-view inputs to improve handling of occlusions and depth ambiguities presents a promising direction.

In conclusion, this paper sets a new benchmark for long-term human motion prediction by anchoring predictions in real-world environmental contexts, thereby addressing fundamental limitations in prior human motion prediction models.