Scene-Aware 3D Human Motion Forecasting via Mutual Distance Prediction

Temel Kavramlar

The core message of this paper is to propose a novel mutual distance representation that explicitly models the interaction between the human body and the 3D scene, enabling more accurate and coherent prediction of future human motion.

Özet

The paper introduces a scene-aware human motion forecasting approach that leverages a mutual distance representation to constrain the whole-body motion of the human both locally and globally. The key contributions are:

1. Mutual Distance Representation:

   • Per-vertex signed distance: Captures the minimum distance from each human body vertex to the scene surface, providing constraints on the local pose.
   • Per-basis point distance: Captures the minimum distance from a set of predefined basis points to the human surface, providing constraints on the global human motion.
   • The combination of these two components ensures coherent constraints on the whole-body motion.

2. Prediction Pipeline:

   • The model first predicts the future mutual distances, then uses the predicted distances to forecast the future human motion.
   • A consistency loss is introduced to ensure the predicted human motion is aligned with the predicted mutual distances.

3. Experiments:

   • The proposed approach is evaluated on several synthetic and real-world datasets, including GTA-IM, PROX, HUMANISE, and GIMO.
   • The results demonstrate that the proposed method consistently outperforms state-of-the-art scene-aware human motion forecasting approaches across various metrics.

İstatistikler

"The per-vertex signed distance can be computed as, d_k^t = -min_y∈∂S ‖v_k^t-y‖_2 if v_k^t∈S; min_y∈∂S ‖v_k^t-y‖_2 if v_k^t∉S, where S is the set of points occupied by the scene and ∂S is the surface points of such scene."
"The per-basis point distance can be computed as, b_p^t = min_y∈∂H^t ‖p_p-y‖_2, where ∂H^t is the human surface at time t."

Alıntılar

"To constrain the whole-body motion for better prediction, in this paper, we propose a mutual distance representation which captures the distances between the human body and the scene."
"While recent works have demonstrated that explicit constraints on human-scene interactions can prevent the occurrence of ghost motion, they only provide constraints on partial human motion e.g., the global motion of the human or a few joints contacting the scene, leaving the rest of unconstrained."

Önemli Bilgiler Şuradan Elde Edildi

Scene-aware Human Motion Forecasting via Mutual Distance Prediction

by Chaoyue Xing... : arxiv.org 04-05-2024

https://arxiv.org/pdf/2310.00615.pdf

Daha Derin Sorular

How can the proposed mutual distance representation be extended to handle dynamic scenes or scenes with moving objects

The proposed mutual distance representation can be extended to handle dynamic scenes or scenes with moving objects by incorporating a dynamic element into the mutual distance calculation. One approach could be to introduce a temporal component to the mutual distance representation, where the distances are not only calculated based on the current static scene but also take into account the movement of objects or changes in the scene over time. This could involve updating the mutual distances at each time step based on the evolving scene dynamics. Additionally, the mutual distance representation could be augmented with velocity information to capture the speed and direction of movement in the scene, allowing for more accurate predictions in dynamic environments.

What are the potential applications of the scene-aware human motion forecasting beyond the ones mentioned in the paper, and how could the method be adapted to those scenarios

The scene-aware human motion forecasting method has several potential applications beyond those mentioned in the paper. One application could be in sports analytics, where the method could be used to predict the movements of athletes in various sports scenarios, aiding in performance analysis and strategy development. Another application could be in healthcare, where the method could assist in rehabilitation programs by forecasting patient movements and providing feedback on correct posture and movement patterns. Furthermore, in the field of robotics, the method could be applied to improve human-robot interaction by predicting human motions in real-time and enabling robots to anticipate and respond to human actions effectively. To adapt the method to these scenarios, the training data could be tailored to the specific domain, and the model architecture could be fine-tuned to capture the nuances of the particular application.

How could the method be further improved to handle long-term motion prediction, where the human-scene interactions become more complex over time

To improve the method for long-term motion prediction, where human-scene interactions become more complex over time, several enhancements could be considered. One approach could be to incorporate attention mechanisms into the model to focus on relevant parts of the scene and human body during the prediction process. This would allow the model to adaptively attend to different aspects of the scene and human motion based on their importance for the prediction task. Additionally, introducing a memory component to the model could help capture long-term dependencies in the data and improve the model's ability to forecast complex interactions over extended time periods. Furthermore, leveraging reinforcement learning techniques to optimize the model's predictions based on feedback from the environment could enhance the method's performance in handling long-term motion forecasting tasks.