Generating Realistic 3D Human-Object Interactions from Text Descriptions in a Zero-Shot Setting

Core Concepts

This paper introduces a novel framework, InterDreamer, that can generate realistic and coherent 3D human-object interaction sequences from text descriptions without requiring text-interaction paired data for training.

Abstract

The paper presents a novel framework, InterDreamer, that can generate 3D human-object interaction sequences from text descriptions in a zero-shot setting, without requiring text-interaction paired data for training. The key insights are:

Interaction semantics and dynamics can be decoupled. The high-level semantics of an interaction, aligned with its textual description, can be informed by human motion and the initial object pose. The low-level dynamics of the interaction, specifically the subsequent behavior of the object, are governed by the forces exerted by the human, within the constraints of physical laws.

The semantics of interaction can be harnessed from a variety of prior knowledge that is independent of text-interaction pair datasets, such as a large language model (LLM) and a pre-trained text-to-motion model.

The dynamics of the interaction can be learned from motion capture data without the necessity of text annotations, by developing a novel world model that predicts the subsequent state of an object affected by the interaction.

The framework integrates these components to generate text-aligned 3D human-object interaction sequences in a zero-shot manner. Experiments on the BEHAVE and CHAIRS datasets demonstrate the capability of InterDreamer to generate realistic and coherent interaction sequences that seamlessly align with the text directives.

Stats

Text-driven human motion generation has made significant progress through advancements in diffusion models.
Existing explorations on text-guided human interaction generation are limited in that the dynamics of objects are not involved or cannot be controlled by text.
Scaling supervised methods to address complex human-object interactions is challenging due to the limited size of existing 3D HOI datasets compared to text-motion datasets.

Quotes

"Text-guided human motion generation [94] has made unprecedented progress through advancements in diffusion models [31,85,86], leading to synthesis outcomes that are more realistic, diverse, and controllable."
"An intriguing question naturally emerges: what is the potential of zero-shot learning for text-conditioned HOI generation, which is the main focus of this paper."

Key Insights Distilled From

InterDreamer

by Sirui Xu,Ziy... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19652.pdf

Deeper Inquiries

How can the dynamics modeling in InterDreamer be further improved to enhance physical plausibility and realism of the generated interactions?

In order to enhance the physical plausibility and realism of the generated interactions in InterDreamer, the dynamics modeling can be further improved in several ways:

Incorporating Physics-Based Constraints: By integrating more detailed physics-based constraints into the dynamics model, such as friction, gravity, and object properties, the interactions can be more accurately simulated to reflect real-world dynamics.

Fine-Tuning Object Dynamics: Refining the modeling of how human actions influence object motion can lead to more realistic interactions. This could involve capturing subtle nuances in object responses to different types of forces exerted by the human.

Enhancing Contact Modeling: Improving the modeling of contact between the human and object surfaces can contribute to more accurate and physically plausible interactions. This could involve refining the contact points and ensuring realistic interactions at these points.

Iterative Optimization: Implementing iterative optimization processes to refine the generated interactions based on physical constraints can help in achieving more realistic and physically plausible outcomes. This could involve adjusting the generated motions to align with physical laws and constraints.

Integrating Feedback Loops: Incorporating feedback loops that allow the system to learn from discrepancies between the generated interactions and real-world interactions can lead to continuous improvement in the dynamics modeling.

How can the framework be extended to handle more complex human-object interactions, such as those involving multiple objects or more intricate hand-object manipulations?

To extend the InterDreamer framework to handle more complex human-object interactions, such as those involving multiple objects or intricate hand-object manipulations, the following strategies can be implemented:

Multi-Object Interactions: Introducing mechanisms to model interactions involving multiple objects simultaneously. This could involve extending the dynamics model to account for interactions between multiple objects and the human, considering constraints like object-object collisions and interactions.

Hierarchical Action Planning: Implementing a hierarchical action planning system that can generate coordinated actions for different body parts interacting with multiple objects. This would involve breaking down the interaction into sub-actions for each object and coordinating them to achieve the desired overall interaction.

Object Affordances: Incorporating knowledge about object affordances to guide the interaction generation process. Understanding how different objects can be manipulated and interacted with can help in generating more realistic and contextually appropriate interactions.

Fine-Grained Hand-Object Interactions: Enhancing the modeling of hand-object interactions by considering finer details such as finger movements, grasping forces, and object manipulation techniques. This level of detail can lead to more intricate and realistic hand-object interactions.

Learning from Diverse Interactions: Training the framework on a diverse dataset that includes a wide range of complex human-object interactions can help in capturing the variability and complexity of real-world interactions. This can improve the framework's ability to generalize to novel and complex scenarios.