Expotion : Face Expression and Motion Control for Video Background Music Generation

Bringing Cartoons to Life with AI-Generated Music

Source: Youtube

Background

Imagine watching your favorite cartoon, where the music doesn’t just play in the background but dynamically changes to match every movement and emotion of the characters. When a character slips on a banana peel, a quirky tune plays; when they feel sad, a somber melody underscores the scene. Cartoons like Tom and Jerry and Shaun the Sheep have long mastered the art of aligning music with motion and expression, creating immersive experiences without relying heavily on dialogue.

Creating such high-quality, synchronized music is both time-consuming and expensive. Professional composers meticulously craft soundtracks to match the timing and emotion of each scene — a labor-intensive process. With the rise of social media platforms like TikTok, where music plays a crucial role in content engagement, there’s a growing demand for accessible, adaptable music that enhances video content without the hefty price tag.

Source: Youtube

What if we could automate this process using artificial intelligence? What if AI could generate music that perfectly aligns with a video’s visuals, capturing every nuance of motion and emotion, all without human intervention? That’s the vision behind my research: Motion and Expression Controlled Video Music Generation for Cartoons.

In our work, we focus on creating an AI system that can generate music in real-time, synchronized with the characters’ movements and expressions in cartoons. To achieve this, we use a dataset of 30 hours of Tom and Jerry episodes, known for their expressive animation and dynamic musical scores. By analyzing this rich content, we aim to teach the AI how to associate certain movements and expressions with appropriate musical responses.

The Proposed System

So, how does our system work? Let’s break it down step by step.

Inference process: the system takes a video as input and music as output.

Capturing the Essence of Video

First, we take a 10-second clip from a cartoon, processing it at five frames per second. This gives us 50 frames to analyze. For each frame, we extract detailed information about the characters:

• Facial Expressions: Is the character happy, sad, surprised, or angry?
• Body Movements and Hand Gestures: Are they jumping, running, waving, or standing still?

To capture this information, we use advanced technologies:

• OpenPose: An open-source tool that detects and tracks human body movements and hand gestures in videos. It allows us to create digital “skeletons” of the characters, mapping out their poses and movements frame by frame [Cao et al., 2019].
• MARLIN (Masked Autoencoder for facial video Representation LearnINg): A cutting-edge method for understanding facial expressions. MARLIN learns to represent faces by reconstructing masked regions of facial videos, capturing both local and global facial features without needing labeled data [Narayan et al., 2022]. This helps the AI recognize subtle changes in expressions.

Creating a Unified Understanding

Next, we combine the information from facial expressions and body movements into a single, unified representation. This involves aligning the different types of data into one cohesive format, allowing the AI to understand the overall scene.

To keep the data manageable, we reduce its dimensions — a process known as dimensionality reduction. This simplifies the data without losing essential details, making it easier for the AI to process and learn from it efficiently.

Training the AI to Generate Music

Now, we train the AI to generate music that matches the video. We use:

• MusicGen: An AI model developed for music generation. MusicGen has been trained on vast amounts of music data and can create new compositions based on given inputs.
• Parameter-Efficient Fine-Tuning (PEFT): A technique that allows us to adapt large AI models like MusicGen to new tasks efficiently, without the need for extensive computational resources.

By feeding the simplified video representation into MusicGen and fine-tuning it using PEFT, the AI learns to compose music that aligns with the characters’ motions and expressions. This process enables the AI to generate music that changes dynamically with the visuals, much like in Tom and Jerry and Shaun the Sheep.

Related Technologies

Several key technologies make this system possible:

MusicGen

MusicGen is like a virtual composer that can create original music based on specific inputs. Trained on a diverse range of songs, it understands different genres, instruments, and styles, enabling it to produce music that fits various moods and actions.

Parameter-Efficient Fine-Tuning (PEFT)

PEFT allows us to fine-tune large AI models efficiently. It’s akin to customizing a pre-tailored suit to fit perfectly, rather than sewing one from scratch, saving time and computational resources.

OpenPose

OpenPose is an open-source tool that detects human poses, including body movements and hand gestures, from images and videos. It provides detailed skeletons of the characters’ movements, which are essential for understanding the action in each frame [Cao et al., 2019].

MARLIN

MARLIN (Masked Autoencoder for facial video Representation LearnINg) is a method for learning facial representations from videos without the need for labeled data. By reconstructing masked regions of faces, MARLIN captures the nuances of facial expressions, which is crucial for generating emotionally appropriate music [Narayan et al., 2022].

The Impact and Future Applications

Our system has the potential to transform how music is integrated into video content:

• For Animators and Filmmakers: It offers a tool to automatically generate music that perfectly syncs with the animation, reducing production time and costs.
• For Social Media Creators: Users can have personalized soundtracks that adapt to their videos’ content, making their posts more engaging and unique.
• For the Entertainment Industry: It opens up possibilities for interactive media where music adapts in real-time to the characters’ actions and emotions.

Imagine creating a video where the soundtrack isn’t just an afterthought but an integral part of the storytelling, generated seamlessly by AI.

Conclusion

By leveraging advanced AI technologies like MusicGen, PEFT, OpenPose, and MARLIN, we’re pioneering a new way to blend music with visual storytelling. Our research aims not just to automate music creation but to enhance the emotional and narrative depth of cartoons and videos.

This fusion of AI and animation brings us closer to a future where music and visuals are more intimately connected, enriching the viewer’s experience. As we continue to develop this technology, we look forward to sharing more breakthroughs and bringing this innovative approach to life.

Thank you for joining me on this journey into AI-driven music generation for cartoons and videos. Stay tuned for more updates as we continue to explore this exciting frontier!

References

• Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., & Sheikh, Y. (2019). OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172–186.
• Narayan, S., Saha, S., Prakash, A., Aich, S., Chakraborty, A., & Panda, R. (2022). MARLIN: Masked Autoencoder for Facial Video Representation Learning. arXiv preprint arXiv:2211.06627. Retrieved from https://arxiv.org/abs/2211.06627
• MusicGen: MusicGen: A Simple and Controllable Music Generation Baseline
• Li, X.L., & Liang, P. (2021). Prefix-Tuning: Optimizing Continuous Prompts for Generation. arXiv preprint arXiv:2101.00190.