InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation
InterAct enhances 3D human-object interaction data quality with a unified optimization framework, expanding the dataset to 30.70 hours.
Key Findings
Methodology
InterAct employs a unified optimization framework, consolidating 21.81 hours of multi-source data, enhancing data quality by reducing artifacts and correcting hand motions. Using the principle of contact invariance, it expands the dataset to 30.70 hours and defines six benchmarking tasks, developing a unified generative modeling perspective.
Key Results
- The dataset expanded to 30.70 hours, providing 217 object types, significantly improving generative model performance.
- Achieved state-of-the-art performance across six benchmarking tasks, validating the dataset's utility.
- Extensive experiments demonstrate the dataset's foundational role in advancing 3D human-object interaction generation.
Significance
InterAct addresses the limitations of existing datasets in scale and annotation quality, providing a robust foundation for 3D interaction generation. Its methodological innovation lies in generating synthetic data through the principle of contact invariance, enhancing generative model performance.
Technical Contribution
InterAct significantly reduces artifacts such as contact penetration and inaccurate hand motions in existing datasets through a unified optimization framework. The introduction of the contact invariance principle offers a new approach to generating synthetic data, enhancing model generation capabilities.
Novelty
InterAct is the first to apply the contact invariance principle to expand 3D human-object interaction datasets, providing a unified optimization framework to enhance data quality, distinctly different from existing methods.
Limitations
- The dataset may still lack interactions with certain object types, affecting model generalization.
- The optimization framework may have limited adaptability to specific scenarios.
Future Work
Future research could explore more complex multi-object interaction scenarios, further enhancing dataset diversity and quality. Additionally, developing more efficient optimization algorithms to adapt to various application scenarios.
AI Executive Summary
3D human-object interaction generation is crucial in robotics, animation, and computer vision, but existing datasets fall short in scale and quality. InterAct integrates multi-source data and proposes a unified optimization framework, significantly enhancing data quality and scale. Its core technology includes the principle of contact invariance, allowing motion variations while maintaining human-object relationships, expanding the dataset to 30.70 hours. Experimental results show that InterAct achieves state-of-the-art performance across six benchmarking tasks, validating its utility as a foundational resource for 3D interaction generation. Nonetheless, the dataset still has room for improvement in interactions with certain object types, and future research could explore more complex interaction scenarios.
Deep Analysis
Background
3D human-object interaction generation has gained attention in recent research, particularly in robotics and animation. However, existing datasets significantly lack in scale and annotation quality, limiting the performance improvement of generative models.
Core Problem
Existing datasets often lack high-quality motion and annotations, exhibiting artifacts such as contact penetration, floating, and inaccurate hand motions, making it challenging for generative models to achieve realism.
Innovation
InterAct addresses dataset artifacts through the principle of contact invariance and a unified optimization framework, expanding the dataset's scale and diversity.
Methodology
- �� Consolidate 21.81 hours of multi-source data, standardizing processing
- �� Use a unified optimization framework to reduce artifacts
- �� Expand the dataset using the principle of contact invariance
- �� Define six benchmarking tasks, developing a unified generative modeling perspective
Experiments
The experimental design includes performance evaluation across six benchmarking tasks, using the expanded dataset for training and testing, validating the dataset's utility in generative models.
Results
Experimental results show that InterAct achieves state-of-the-art performance across six benchmarking tasks, significantly enhancing generative model performance.
Applications
The InterAct dataset can be directly applied in 3D human-object interaction generation in robotics, animation, and computer vision, enhancing technological levels in these fields.
Limitations & Outlook
While InterAct has made breakthroughs in data quality and scale, there is still room for improvement in interactions with certain object types, and future research could explore more complex interaction scenarios.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. You need to pick up pots, chop vegetables, and stir soup. InterAct is like a super-smart kitchen assistant that not only helps you pick things up but also tells you how to perform these actions better. It learns these actions by observing how you interact with objects and then generates smoother, more natural sequences of actions.
ELI14 Explained like you're 14
Imagine you're playing a 3D game where your character needs to interact with various objects, like picking up a sword or opening a door. InterAct is like an AI assistant in the game that makes these actions look more realistic and natural. It learns these actions from a big dataset and then generates better animation effects. Isn't that cool?
Glossary
Contact Invariance
A principle that maintains human-object contact relationships unchanged, used for generating synthetic data.
Used in generating synthetic data to expand the dataset.
Generative Model
A model used to generate new data, typically trained on existing data.
Used for generating 3D human-object interaction sequences.
Optimization Framework
A systematic method for improving data quality by reducing artifacts.
Used for enhancing the quality of the dataset.
Artifact
Unrealistic phenomena in data, such as contact penetration and inaccurate hand motions.
Common issues in existing datasets.
Benchmark Task
Standardized tasks used to evaluate model performance.
Used to validate the effectiveness of the dataset and generative models.
Open Questions Unanswered questions from this research
- 1 How to maintain high-quality generation effects in multi-object interaction scenarios? Existing methods perform limitedly in complex scenarios.
Applications
Immediate Applications
Robotic Interaction
Enhance the naturalness and smoothness of robot-environment interactions, applicable to service robots.
Long-term Vision
Virtual Reality
Enhance the immersion and interactivity of virtual reality experiences through more realistic 3D interactions.
Abstract
While large-scale human motion capture datasets have advanced human motion generation, modeling and generating dynamic 3D human-object interactions (HOIs) remain challenging due to dataset limitations. Existing datasets often lack extensive, high-quality motion and annotation and exhibit artifacts such as contact penetration, floating, and incorrect hand motions. To address these issues, we introduce InterAct, a large-scale 3D HOI benchmark featuring dataset and methodological advancements. First, we consolidate and standardize 21.81 hours of HOI data from diverse sources, enriching it with detailed textual annotations. Second, we propose a unified optimization framework to enhance data quality by reducing artifacts and correcting hand motions. Leveraging the principle of contact invariance, we maintain human-object relationships while introducing motion variations, expanding the dataset to 30.70 hours. Third, we define six benchmarking tasks and develop a unified HOI generative modeling perspective, achieving state-of-the-art performance. Extensive experiments validate the utility of our dataset as a foundational resource for advancing 3D human-object interaction generation. To support continued research in this area, the dataset is publicly available at https://github.com/wzyabcas/InterAct, and will be actively maintained.