VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis introduces a sophisticated technique for video generation from a single still image, by utilizing multimodal diffusion models. It encapsulates a 3D motion diffusion model and an architecture supporting temporal control, fostering the generation of varied-length, high-quality videos.

Key takeaways:

• Showcases advancement in audio-driven video generation technology.
• Does not require per-person training, nor relies on face detection/cropping.
• Introduces MENTOR, a dataset 10x larger than previous ones, enhancing diversity.
• Performs above the state-of-the-art in benchmarks measuring different facets of video synthesis quality.

This work signifies a leap in video editing and personalization applications, demonstrating the power of multimodal diffusion models in synthesizing dynamic and realistic human avatars.