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


Core Concepts
Proposing THOR, a diffusion model for generating human-object interactions from textual descriptions with relation intervention.
Abstract
The paper introduces THOR, a novel model for generating dynamic Human-Object Interactions from textual descriptions. It addresses challenges like human motion variation, object shape diversity, and semantic vagueness. THOR integrates text-guided human and object motion with relation intervention to enhance spatial-temporal relations. The model introduces interaction losses at different motion granularity levels and constructs the Text-BEHAVE dataset. Experimental results demonstrate the effectiveness of THOR in generating realistic interactions.
Stats
"Text2HOI dataset that seamlessly integrates textual descriptions with the currently largest publicly available 3D HOI dataset." "Total of 2377 interaction clips ranging from 2 to 10s." "2144 clips for training and 233 clips for testing."
Quotes
"A person moves the stool with his two hands counterclockwise and then puts it down." "A person lifts a large box from the ground and inspects it." "A person grabs the wood chair’s back and turns around."

Key Insights Distilled From

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

https://arxiv.org/pdf/2403.11208.pdf
THOR

Deeper Inquiries

How can THOR's intervention mechanism improve the generation of human-object interactions?

THOR's intervention mechanism improves the generation of human-object interactions by leveraging the spatial relations between humans and objects to refine object motion. Instead of predicting object motion directly from textual descriptions, which may lead to inconsistencies, THOR uses the human-centric relation representation to predict residual correction terms on object motion. By focusing on how objects move relative to human body poses, this approach enhances contextual awareness and generates more realistic and consistent interactions. The intervention mechanism ensures that the generated object motion aligns with the provided textual guidance, resulting in more accurate and plausible human-object interactions.

What are the implications of integrating textual descriptions into datasets like Text-BEHAVE?

Integrating textual descriptions into datasets like Text-BEHAVE has several implications for research in 3D Human-Object Interaction (HOI) synthesis: Enhanced Data Annotation: Textual descriptions provide additional context and semantic information about interactions, enriching dataset annotations. Improved Model Training: Models trained on text-integrated datasets can learn to generate more meaningful and coherent human-object interactions guided by natural language prompts. Real-world Applications: Datasets like Text-BEHAVE enable advancements in VR/AR applications, embodied AI systems, computer animation, robotics, and other fields where understanding HOIs is crucial. Benchmarking Performance: Researchers can use text-guided datasets for benchmarking model performance in generating dynamic HOIs from textual prompts.

How might incorporating dexterous hand motion enhance human-object interaction synthesis?

Incorporating dexterous hand motion into human-object interaction synthesis can significantly enhance the realism and complexity of generated interactions: Fine-grained Interactions: Dexterous hand motions allow for precise manipulation of objects during interactions, leading to more detailed and nuanced behaviors. Increased Realism: Including hand gestures such as grasping, holding, releasing, or manipulating objects adds a layer of realism that mimics real-world scenarios. Expanded Interaction Repertoire: With dexterous hand motions integrated into synthesis models, a wider range of interactive behaviors involving intricate hand movements can be simulated. Contextual Understanding: Hand gestures often convey specific intentions or actions within an interaction context; incorporating them provides deeper insights into user intentions or behavior cues during interaction synthesis. By incorporating dexterous hand motions into human-object interaction synthesis models, researchers can achieve higher fidelity simulations that better reflect real-world dynamics between humans and objects.
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