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THOR: Text to Human-Object Interaction Diffusion via Relation Intervention

Core Concepts
Proposing THOR, a diffusion model for generating dynamic Human-Object Interactions from textual descriptions, integrating human-object interactions and intervention mechanisms.
Introduction: Discusses the importance of synthesizing human-object interactions. Method: Details the Text2HOI diffusion framework and the human-object relation intervention. Experiments: Evaluates the model on the Text-BEHAVE dataset with quantitative and qualitative results. Conclusion: Summarizes the contributions and outlines future directions.
"Both quantitative and qualitative experiments demonstrate the effectiveness of our proposed model." "The total interaction length amounts 440,840 frames at 30 fps."
"THOR is a cohesive diffusion model equipped with a relation intervention mechanism." "Our contributions include proposing THOR tailored for Text2HOI and introducing supervision on kinematic relations."

Key Insights Distilled From

by Qianyang Wu,... at 03-19-2024

Deeper Inquiries

How can the model address issues like penetration and floating in generated results?

The model can address issues like penetration and floating in generated results by incorporating intervention mechanisms and interaction losses. The intervention mechanism leverages human-centric relations to refine object motion, ensuring that the object's rotations and translations align with human poses. This helps prevent unrealistic interactions such as penetration or floating by correcting implausible object motions based on the spatial relations between humans and objects. Additionally, interaction losses supervise kinematic relations and geometric distances between humans and objects at multiple levels, encouraging the generation of reasonable spatial configurations. By integrating these components into the diffusion framework, the model can produce more coherent and realistic human-object interactions.

What are potential limitations of existing HOI datasets compared to human motion datasets?

Existing HOI datasets may have limitations compared to human motion datasets in terms of scale, quality, diversity, and complexity. One limitation is that HOI datasets often lack extensive coverage of diverse interactions involving various objects across different scenarios. Human motion datasets typically focus on capturing a wide range of movements with detailed annotations for joints and poses but may not provide sufficient data on complex interactions with dynamic objects. Another limitation could be the scarcity of high-quality annotations for fine-grained actions involving intricate hand-object manipulations or nuanced gestures within HOI datasets compared to comprehensive annotations available in human motion datasets.

How could integrating dexterous hand motion enhance human-object interaction generation?

Integrating dexterous hand motion into human-object interaction generation models can significantly enhance the realism and richness of synthesized interactions. Dexterous hand motions involve intricate finger movements, grasping actions, object manipulation skills, etc., which play a crucial role in how humans interact with objects in real-world scenarios. By incorporating detailed representations of dexterous hand motions into the synthesis process, models can capture subtle nuances in how hands interact with objects during activities like picking up items, manipulating tools, or performing delicate tasks. This integration enables more accurate simulation of realistic behaviors during human-object interactions while adding an extra layer of authenticity to generated results.