Generating Diverse and Plausible Human-Scene Interactions from Text Prompts

Core Concepts

A method for generating realistic and diverse human-object interactions in 3D scenes, controlled by text prompts.

Abstract

The paper presents TeSMo, a novel approach for generating text-controlled, scene-aware human motions. The key insights are:

Leveraging a pre-trained text-to-motion diffusion model as the base, and fine-tuning it with an augmented scene-aware component to enable scene-awareness while maintaining text controllability.

Decomposing the motion generation task into two stages - navigation and interaction. The navigation model generates a scene-aware root trajectory that reaches a goal location near the target object, which is then lifted to a full-body motion. The interaction model directly generates the full-body motion conditioned on the start pose, goal pose, and 3D object geometry.

Creating specialized datasets for training the scene-aware components - Loco-3D-FRONT for navigation and an extended SAMP dataset with text annotations for interactions.

The experiments demonstrate that TeSMo outperforms prior scene-aware and scene-agnostic methods in terms of goal-reaching, collision avoidance, and the plausibility of generated human-object interactions, while preserving the realism and text-following ability of the base diffusion model.

Stats

The paper reports the following key metrics:

For navigation, the goal-reaching error in position (0.169 m), orientation (0.119 rad), and height (0.008 m), with a collision ratio of 0.031.
For full-body motion after in-painting, the FID score is 20.465, R-precision is 0.376, and diversity is 6.415.
For interactions, the goal-reaching error in position (0.1445 m), height (0.012 m), and orientation (0.241 rad), with an average penetration value of 0.0043 and penetration ratio of 0.0611.

Quotes

"Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets. We then enhance this model with a scene-aware component, fine-tuned using data augmented with detailed scene information, including ground plane and object shapes."
"To facilitate training, we embed annotated navigation and interaction motions within scenes."

Key Insights Distilled From

Generating Human Interaction Motions in Scenes with Text Control

by Hongwei Yi,J... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10685.pdf

Deeper Inquiries

How could the navigation and interaction components be further integrated into a single end-to-end model to improve the overall coherence of the generated motions?

Integrating the navigation and interaction components into a single end-to-end model can enhance the coherence of the generated motions by allowing for a more seamless transition between navigation and interaction phases. One approach to achieve this integration is to design a unified model architecture that can jointly optimize for both navigation and interaction tasks. Here are some key steps to consider:

Shared Representation: Create a shared representation space that captures both the navigation and interaction aspects of the motion generation process. This shared representation should encode information about the scene, the character's state, the target object, and the desired action.

Hierarchical Planning: Implement a hierarchical planning mechanism where the model can first plan the navigation trajectory to reach the target object and then seamlessly transition to generating the interaction motion with the object. This hierarchical structure can ensure a smooth and coherent sequence of actions.

Multi-Task Learning: Train the model using multi-task learning techniques to jointly optimize for navigation and interaction tasks. By sharing parameters and learning objectives across both components, the model can better capture the dependencies between navigation and interaction behaviors.

Feedback Mechanisms: Incorporate feedback mechanisms that allow the model to adjust the generated motions based on the success of the navigation phase in reaching the target object. This feedback loop can help refine the interaction motions to better align with the scene context and user input.

Dynamic Object Handling: Develop mechanisms to handle dynamic objects in the scene, such as predicting their movements or adapting the interaction motions in real-time. This capability can enhance the realism and adaptability of the generated motions in dynamic environments.

By integrating navigation and interaction components into a unified end-to-end model, the overall coherence and realism of the generated motions can be significantly improved, leading to more natural and contextually-aware human-scene interactions.

What are the potential limitations of the current approach in handling more complex interactions, such as those involving dynamic objects or multi-agent scenarios?

While the current approach shows promise in generating realistic human-scene interactions, there are potential limitations when handling more complex interactions, especially those involving dynamic objects or multi-agent scenarios. Some of the limitations include:

Dynamic Object Interactions: The current model may struggle to adapt to dynamic objects that change position or properties during the interaction. Predicting the behavior of dynamic objects in real-time and coordinating human motions accordingly can be challenging and may require more sophisticated modeling techniques.

Real-time Adaptation: In scenarios with dynamic objects or multi-agent interactions, the model may need to adapt its generated motions on-the-fly based on changing environmental conditions or agent behaviors. This real-time adaptation capability is crucial for handling dynamic and unpredictable scenarios effectively.

Complex Scene Understanding: Handling multi-agent scenarios requires a deep understanding of the interactions between different agents and their environment. The current model may face challenges in capturing the intricate dynamics and dependencies in such scenarios, leading to less coherent or realistic motion generation.

Generalization to Novel Scenarios: The model's ability to generalize to unseen or complex scenarios with varying object properties, agent behaviors, or scene configurations may be limited. Ensuring robust performance in diverse and challenging environments requires extensive training data and sophisticated modeling techniques.

Scalability and Efficiency: As the complexity of interactions increases, the computational demands and training requirements of the model may also escalate. Ensuring scalability and efficiency while handling complex interactions is essential for practical deployment in real-world applications.

Addressing these limitations would require advancements in model architecture, training strategies, and data augmentation techniques to enhance the model's capability to handle more complex interactions involving dynamic objects and multi-agent scenarios effectively.

How could the proposed framework be extended to generate motions for other types of characters, such as animals or robots, in addition to humans?

Extending the proposed framework to generate motions for other types of characters, such as animals or robots, involves adapting the model architecture, training data, and input representations to accommodate the unique characteristics and movement patterns of these entities. Here are some key considerations for extending the framework:

Diverse Training Data: Curate a diverse dataset that includes motion capture data for animals or robots, along with corresponding text descriptions and scene contexts. This data should cover a wide range of movements specific to the target characters.

Character-specific Representations: Modify the input representations and model architecture to account for the anatomical differences and locomotion patterns of animals or robots. This may involve incorporating specialized joint structures, movement constraints, and environmental interactions.

Behavioral Modeling: Develop specialized modules or components within the model that capture the unique behaviors and characteristics of animals or robots. This could include adapting the navigation and interaction components to reflect the natural movements and interactions of the target characters.

Domain Transfer Learning: Explore techniques such as domain transfer learning to leverage knowledge from human motion generation and adapt it to generate motions for animals or robots. Transfer learning can help the model generalize across different character types effectively.

Evaluation and Validation: Establish specific evaluation metrics and validation criteria tailored to the characteristics of animals or robots. This ensures that the generated motions are realistic, coherent, and aligned with the intended behaviors of the target characters.

By incorporating these considerations and modifications, the proposed framework can be extended to generate diverse and contextually-aware motions for a wide range of characters, including animals and robots, opening up new possibilities for interactive and dynamic simulations in various applications.