# Semantic Alignment

MuVi is an innovative framework that analyzes video content to extract contextually and temporally relevant features, generating music that aligns with the mood, theme, rhythm, and tempo of the video. This framework implements a comparative music-visual pre-training scheme to ensure the periodic synchronization of musical phrases, and showcases the capabilities of a flow-matching-based music generator with contextual learning, allowing for control over the style and type of generated music. MuVi demonstrates superior performance in audio quality and temporal synchronization, providing new solutions for the integration of audio and video content and enhancing immersive experiences.

vta-ldm is a deep learning model focused on video-to-audio generation. It can generate audio content semantically and temporally aligned with the video input. It represents a new breakthrough in the field of video generation, especially following the significant progress made in text-to-video generation technology. Developed by Manjie Xu and others at the Tencent AI Lab, the model has the ability to generate audio that is highly consistent with video content, and has important application value in video production, audio post-processing, and other fields.

ELLA (Efficient Large Language Model Adapter) is a lightweight method that equips existing CLIP-based diffusion models with powerful LLMs. ELLA enhances the model's prompt following capability, enabling text-to-image models to understand long texts. We designed a Time-Sensitive Semantic Connector (TSC) to extract various denoising stage time-step related conditioning from pre-trained LLMs. Our TSC dynamically adapts semantic features for different sampling time steps, helping to freeze U-Net at different semantic levels. ELLA outperforms benchmarks like DPG-Bench, particularly in dense prompting scenarios involving multiple object combinations, diverse attributes, and relationships.