  The harmonious integration of music with dance movements is pivotal in
vividly conveying the artistic essence of dance. This alignment also
significantly elevates the immersive quality of gaming experiences and
animation productions. While there has been remarkable advancement in creating
high-fidelity music from textual descriptions, current methodologies mainly
concentrate on modulating overarching characteristics such as genre and
emotional tone. They often overlook the nuanced management of temporal rhythm,
which is indispensable in crafting music for dance, since it intricately aligns
the musical beats with the dancers' movements. Recognizing this gap, we propose
an encoder-based textual inversion technique for augmenting text-to-music
models with visual control, facilitating personalized music generation.
Specifically, we develop dual-path rhythm-genre inversion to effectively
integrate the rhythm and genre of a dance motion sequence into the textual
space of a text-to-music model. Contrary to the classical textual inversion
method, which directly updates text embeddings to reconstruct a single target
object, our approach utilizes separate rhythm and genre encoders to obtain text
embeddings for two pseudo-words, adapting to the varying rhythms and genres. To
achieve a more accurate evaluation, we propose improved evaluation metrics for
rhythm alignment. We demonstrate that our approach outperforms state-of-the-art
methods across multiple evaluation metrics. Furthermore, our method seamlessly
adapts to in-the-wild data and effectively integrates with the inherent
text-guided generation capability of the pre-trained model. Samples are
available at \url{https://youtu.be/D7XDwtH1YwE}.
