Impact of Domain-Specific Fine-Tuning on Zero-Shot Accuracy of Text-to-Image Models

Abstract

Abstract: Human motion generation is essential for fields such as animation, robotics, and virtual reality, requiring models that effectively capture motion dynamics from text descriptions. Existing approaches often rely on Contrastive Language-Image Pretraining (CLIP)-based text encoders, but their training on text-image pairs constrains their ability to understand temporal and kinematic structures inherent in motion and motion generation. This work introduces MoCLIP, a fine-tuned CLIP model with an additional motion encoding head, trained on motion sequences using contrastive learning and tethering lo

Research Question

To what extent does fine-tuning the semantic alignment module on domain-specific datasets (e.g., medical or scientific images) improve the zero-shot accuracy of text-to-image models on specialized benchmarks?

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