Architecture-Led Analysis of Body Language Detection with VLMs
Published:Dec 28, 2025 18:03
•1 min read
•ArXiv
Analysis
This paper provides a practical analysis of using Vision-Language Models (VLMs) for body language detection, focusing on architectural properties and their impact on a video-to-artifact pipeline. It highlights the importance of understanding model limitations, such as the difference between syntactic and semantic correctness, for building robust and reliable systems. The paper's focus on practical engineering choices and system constraints makes it valuable for developers working with VLMs.
Key Takeaways
- •Highlights the importance of understanding VLM architectural properties for practical applications.
- •Emphasizes the limitations of VLMs, such as the difference between syntactic and semantic correctness.
- •Provides insights into designing robust interfaces and planning evaluation for VLM-based systems.
- •Focuses on the practical aspects of building a video-to-artifact pipeline for body language detection.
Reference
“Structured outputs can be syntactically valid while semantically incorrect, schema validation is structural (not geometric correctness), person identifiers are frame-local in the current prompting contract, and interactive single-frame analysis returns free-form text rather than schema-enforced JSON.”