Leveraging Language Models to Optimize 3D Human Pose Estimation with Accurate Physical Contact Constraints

Core Concepts

Our method leverages the semantic priors of large pretrained language models to convert natural language descriptions of physical interactions into mathematical constraints, which can then be optimized to refine 3D pose estimates and accurately capture self-contact and person-to-person contact.

Abstract

The paper presents a novel zero-shot framework, called ProsePose, that leverages the implicit knowledge of large multimodal language models (LMMs) to improve 3D human pose estimation. The key insight is that since language is often used to describe physical interactions, LMMs can act as priors on pose estimation. The method works as follows: It first obtains an initial 3D pose estimate using a regression-based pose estimation model. It then prompts an LMM (GPT4-V) with the image and a request to identify all pairs of body parts that are touching. The LMM generates a list of contact constraints. These contact constraints are converted into a loss function that can be optimized jointly with other common losses, such as 2D keypoint loss, to refine the initial pose estimates. The authors demonstrate that ProsePose produces more accurate 3D pose reconstructions compared to previous zero-shot approaches on three 2-person interaction datasets and one dataset of complex yoga poses. The method is able to correctly capture self-contact and person-to-person contact without requiring any additional training data or annotations. The key advantages of ProsePose are: It leverages the implicit semantic knowledge in LMMs to guide pose optimization, avoiding the need for expensive contact annotations required by previous methods. It provides a unified framework for resolving both self-contact and person-to-person contact, which are challenging for state-of-the-art pose regression and optimization methods. It is a zero-shot approach that can be applied to new images without any additional training. Overall, the paper demonstrates the potential of using language models as priors for 3D human pose estimation, particularly in scenarios involving physical interactions and contact.

Stats

"Their faces are touching as they lean into each other" "The yogi reaches their hands back to touch their heels."

Quotes

"Our central insight is that since language is often used to describe physical interaction, large pretrained text-based models can act as priors on pose estimation." "We can thus leverage this insight to improve pose estimation by converting natural language descriptors, generated by a large multimodal model (LMM), into tractable losses to constrain the 3D pose optimization."

Key Insights Distilled From

Pose Priors from Language Models

by Sanj... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03689.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex multi-person interactions beyond just pairs of people

The proposed framework can be extended to handle more complex multi-person interactions by incorporating a hierarchical approach to pose estimation. Instead of focusing solely on pairs of people, the framework can be modified to consider interactions involving multiple individuals. This can be achieved by prompting the language model to generate constraints for each pair of individuals in the scene and then aggregating these constraints to capture the overall interaction dynamics. By iteratively refining the pose estimates for each individual based on the collective constraints generated by the language model, the framework can effectively model complex multi-person interactions. Additionally, incorporating a mechanism to handle occlusions and partial visibility of individuals in the scene can further enhance the framework's ability to capture intricate interactions involving multiple people.

What are the potential limitations or biases of using language models as priors for 3D pose estimation, and how can these be mitigated

One potential limitation of using language models as priors for 3D pose estimation is the risk of bias or inaccuracies in the generated constraints. Language models may exhibit biases based on the training data they have been exposed to, which can lead to incorrect or unrealistic constraints being generated. To mitigate this, it is essential to carefully curate the training data for the language model to ensure a diverse and representative set of examples. Additionally, incorporating mechanisms for uncertainty estimation and confidence scoring in the generated constraints can help identify and filter out unreliable or hallucinated constraints. Regularly updating and fine-tuning the language model with new data can also help reduce biases and improve the accuracy of the generated constraints.

How might the insights from this work on leveraging language models for 3D pose estimation apply to other computer vision tasks that involve reasoning about physical interactions and dynamics

The insights from leveraging language models for 3D pose estimation can be applied to other computer vision tasks that involve reasoning about physical interactions and dynamics. For tasks such as action recognition, gesture analysis, and activity understanding, language models can provide valuable priors and constraints to guide the interpretation of visual data. By converting natural language descriptions of actions or interactions into mathematical constraints, similar to the approach proposed in the study, computer vision systems can benefit from the semantic knowledge embedded in language models to improve the accuracy and robustness of their predictions. This approach can enable more context-aware and interpretable models for a wide range of tasks in computer vision that involve understanding human behavior and interactions.