Realistic Long-Term 3D Human Motion Forecasting with Multimodal Scene Context

Alapfogalmak

The authors propose a scene-aware social transformer model (SAST) that can efficiently forecast long-term (10 seconds) human motion in complex multi-person environments by leveraging both motion and scene context information.

Kivonat

The authors present a novel approach for long-term 3D human motion forecasting in multi-person settings with rich scene context. Key highlights:

The model combines a temporal convolutional encoder-decoder architecture with a Transformer-based bottleneck to efficiently combine motion and scene information.
It can handle widely varying numbers of people (1-16) and objects (29-50) in a scene, unlike previous methods.
The model uses denoising diffusion to generate diverse and realistic motion sequences conditioned on the input and context.
Extensive experiments on the Humans in Kitchens dataset show that the proposed approach outperforms state-of-the-art methods in terms of realism and diversity, as validated by both quantitative metrics and a user study.
Ablation studies demonstrate the importance of incorporating both multi-person and scene context for generating coherent and interdependent motion.
The model still has some limitations, such as discontinuities between input and predicted sequences and decreased limb movement realism during long global motion.

Statisztikák

"Forecasting long-term 3D human motion is challenging: the stochasticity of human behavior makes it hard to generate realistic human motion from the input sequence alone."
"Information on the scene environment and the motion of nearby people can greatly aid the generation process."
"Our model allows for long-term (10 seconds) motion forecasting, versatile interaction modeling for widely varying (1-16) numbers of persons, and scene-agnostic environment modeling based only on variable numbers (50+) of 3D object point clouds."

Idézetek

"Jointly forecasting multi-person motion for a large varying number of persons is difficult to learn. We simplify this task by forecasting only one person at a time during training (with context information from other people). During inference, we are able to produce highly interdependent multi-person motion by exchanging motion information throughout the diffusion process."
"To our knowledge, our approach is the first long-term multi-person motion forecasting model that takes scene context into account."

Főbb Kivonatok

Massively Multi-Person 3D Human Motion Forecasting with Scene Context

by Felix B Muel... : arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.12189.pdf

Mélyebb kérdések

How could the model be extended to handle dynamic scene changes, such as moving objects or people entering/leaving the scene during the prediction horizon?

To extend the scene-aware social transformer model (SAST) for handling dynamic scene changes, several strategies could be implemented. First, the model could incorporate a real-time scene update mechanism that continuously integrates new information about moving objects and people entering or leaving the scene. This could be achieved through a recurrent architecture or a sliding window approach that updates the scene context at each prediction step. By maintaining a dynamic representation of the scene, the model can adapt its predictions based on the latest spatial configurations and interactions.
Additionally, integrating a tracking system for moving objects and people could enhance the model's ability to predict interactions more accurately. For instance, using object detection and tracking algorithms, the model could receive updated positions and states of objects and individuals, allowing it to adjust the motion forecasts accordingly. This would enable the model to generate more realistic motion sequences that reflect the ongoing dynamics of the environment.
Moreover, employing a multi-modal approach that combines visual inputs (e.g., video feeds) with the existing 3D object point clouds could provide richer context for the model. This would allow the model to better understand the spatial relationships and interactions in a dynamic scene, leading to improved realism in the generated motion sequences.

What other modalities, beyond 3D object point clouds, could be leveraged to further improve the realism and diversity of the generated motion sequences?

To enhance the realism and diversity of generated motion sequences, several additional modalities could be integrated into the SAST framework. One promising modality is audio signals, which can provide contextual cues about the environment and the actions being performed. For example, sounds associated with specific activities (e.g., cooking, talking) could inform the model about the likely actions of individuals in the scene, leading to more contextually appropriate motion predictions.
Another modality is textual descriptions or action labels that specify the intended actions of individuals. By conditioning the model on these labels, it can generate motion sequences that align more closely with the described activities, thereby increasing the diversity of the generated motions. This could be particularly useful in scenarios where specific actions are expected, such as in a kitchen environment.
Sensor data from wearable devices could also be utilized to capture individual motion dynamics more accurately. For instance, accelerometer and gyroscope data could provide insights into the physical movements of individuals, allowing the model to generate more nuanced and realistic motion sequences.
Lastly, incorporating social context through dialogue or interaction histories could enhance the model's understanding of interpersonal dynamics. By analyzing past interactions, the model could better predict how individuals might respond to each other in various scenarios, leading to more realistic and diverse motion forecasts.

How could the model's performance be improved to better preserve the continuity between input and predicted sequences, and maintain realistic local limb movements during long global motions?

To improve the model's performance in preserving continuity between input and predicted sequences, as well as maintaining realistic local limb movements during long global motions, several strategies can be employed.
First, enhancing the temporal coherence of the predictions is crucial. This could be achieved by implementing a temporal consistency loss during training, which penalizes abrupt changes in motion between the last frame of the input sequence and the first frame of the predicted sequence. By encouraging smoother transitions, the model can generate outputs that are more aligned with the input motion, thereby improving continuity.
Incorporating motion priors or physical constraints into the model could also help maintain realistic limb movements. For instance, using kinematic models that define the allowable ranges of motion for joints can guide the model to produce more plausible limb configurations. This would prevent unrealistic poses that may arise during long global motions.
Additionally, leveraging multi-scale temporal modeling can enhance the model's ability to capture both short-term and long-term dependencies in motion. By employing hierarchical architectures that process motion at different temporal resolutions, the model can better understand the nuances of local limb movements while still considering the broader context of global motion.
Finally, conducting user studies to gather qualitative feedback on the generated motions can provide insights into specific areas for improvement. By analyzing user preferences and identifying common issues in the generated sequences, targeted adjustments can be made to the model to enhance both continuity and realism in local limb movements.