VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis

TL;DR

VLOGGER introduces a novel multimodal diffusion approach for generating high-quality, controllable, and identity-preserving human videos from a single image without person-specific training, advancing the realism and diversity of avatar synthesis.

Contribution

The paper presents a new diffusion-based architecture for audio-driven human video synthesis that does not require per-person training and introduces a large, diverse dataset called MENTOR.

Findings

Outperforms state-of-the-art in image quality, identity preservation, and temporal consistency.

Supports generation of upper-body gestures and diverse scenarios.

Enables applications in video editing and personalization.

Abstract

We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models. Our method consists of 1) a stochastic human-to-3d-motion diffusion model, and 2) a novel diffusion-based architecture that augments text-to-image models with both spatial and temporal controls. This supports the generation of high quality video of variable length, easily controllable through high-level representations of human faces and bodies. In contrast to previous work, our method does not require training for each person, does not rely on face detection and cropping, generates the complete image (not just the face or the lips), and considers a broad spectrum of scenarios (e.g. visible torso or diverse subject identities) that are critical to correctly synthesize humans who communicate. We also curate MENTOR, a new and diverse dataset with 3d pose and expression annotations, one order of magnitude larger than previous ones (800,000 identities) and with dynamic gestures, on which we train and ablate our main technical contributions. VLOGGER outperforms state-of-the-art methods in three public benchmarks, considering image quality, identity preservation and temporal consistency while also generating upper-body gestures. We analyze the performance of VLOGGER with respect to multiple diversity metrics, showing that our architectural choices and the use of MENTOR benefit training a fair and unbiased model at scale. Finally we show applications in video editing and personalization.