Language-guided Human Motion Generation with Scene Affordance

Core Concepts

Utilizing scene affordance as an intermediate representation enhances language-guided human motion generation in 3D environments.

Abstract

The article introduces a novel two-stage framework that employs scene affordance to bridge 3D scene grounding and conditional motion generation. The framework consists of an Affordance Diffusion Model (ADM) for predicting affordance maps and an Affordance-to-Motion Diffusion Model (AMDM) for generating human motions. Extensive experiments demonstrate superior performance on established benchmarks like HumanML3D and HUMANISE, showcasing advanced generalization capabilities. Structure: Introduction Related Work Preliminaries Method Experiments Datasets Metrics and Baselines Results on HumanML3D Results on HUMANISE Results on Novel Evaluation Set Ablation Study Conclusion Acknowledgments References

Stats

Despite advancements in text-to-motion synthesis, challenges remain in generating language-guided human motion in 3D environments. The proposed framework employs scene affordance as an intermediate representation to enhance motion generation capabilities. The Affordance Diffusion Model (ADM) predicts explicit affordance maps, while the Affordance-to-Motion Diffusion Model (AMDM) generates human motions. Extensive experiments show the model consistently outperforms baselines on established benchmarks like HumanML3D and HUMANISE.

Quotes

"Utilizing scene affordance maps, our method overcomes the difficulty in generating human motion under multimodal condition signals." "Our model showcases remarkable generalization capabilities, achieving impressive performance in generating human motions for novel language-scene pairs."

Key Insights Distilled From

Move as You Say, Interact as You Can

by Zan Wang,Yix... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18036.pdf

Deeper Inquiries

How can the reliance on diffusion models impact the scalability of the proposed framework?

The reliance on diffusion models can impact the scalability of the proposed framework in several ways. Firstly, diffusion models are computationally intensive, requiring iterative denoising processes for learning and sampling data distributions. This can lead to slower inference times, which may hinder real-time applications or large-scale deployment. Additionally, the training of diffusion models can be resource-intensive, requiring significant computational power and memory. This can pose challenges in scaling up the framework to handle larger datasets or more complex scenarios. Furthermore, the complexity of diffusion models may require specialized expertise to optimize and fine-tune, potentially limiting the accessibility of the framework to a broader audience.

How might the integration of scene affordance impact the development of future motion generation models?

The integration of scene affordance can have a significant impact on the development of future motion generation models. By utilizing scene affordance as an intermediate representation, future models can benefit from enhanced 3D scene grounding and improved conditional motion generation. This approach allows for more accurate and contextually relevant human motion synthesis, as the affordance maps provide a sophisticated understanding of the geometric interplay between scenes and human motions. Incorporating scene affordance can also improve the generalization capabilities of motion generation models, enabling them to adapt to unseen scenarios and diverse scene geometries. This can lead to more robust and versatile models that can generate human motions in a wider range of environments and scenarios. Additionally, the use of scene affordance can help address challenges related to data scarcity by providing a more structured and informative representation of the scene, reducing the reliance on extensive paired data for training. Overall, the integration of scene affordance in future motion generation models can enhance their performance, generalization capabilities, and efficiency, paving the way for more advanced and context-aware human motion synthesis systems.

What are the potential strategies to overcome the challenge of limited data availability in 3D environments?

Overcoming the challenge of limited data availability in 3D environments is crucial for the development of robust and effective motion generation models. Several strategies can be employed to address this challenge: Data Augmentation: Utilizing data augmentation techniques such as geometric transformations, texture variations, and scene perturbations can help increase the diversity of the training data without the need for additional labeled samples. Transfer Learning: Leveraging pre-trained models or features from related tasks or domains can help bootstrap the training process and improve model performance with limited data. Synthetic Data Generation: Generating synthetic data using procedural methods, simulation environments, or generative models can supplement the existing dataset and provide additional training samples. Semi-Supervised Learning: Incorporating semi-supervised learning techniques that leverage both labeled and unlabeled data can help make more efficient use of the available data and improve model performance. Active Learning: Implementing active learning strategies to intelligently select and label the most informative data points can optimize the data annotation process and maximize the utility of limited labeled data. Data Fusion: Integrating data from multiple sources or modalities, such as text descriptions, 3D scenes, and motion sequences, can enrich the training data and provide a more comprehensive understanding of the task. By combining these strategies and exploring innovative approaches to data augmentation, synthesis, and utilization, researchers can mitigate the impact of limited data availability and enhance the performance and generalization capabilities of motion generation models in 3D environments.