X-Dyna: Expressive Dynamic Human Image Animation
Di Chang, Hongyi Xu, You Xie, Yipeng Gao, Zhengfei Kuang, Shengqu Cai, Chenxu Zhang, Guoxian Song, Chao Wang, Yichun Shi, Zeyuan Chen, Shijie Zhou, Linjie Luo, Gordon Wetzstein, Mohammad Soleymani
2025-01-20
Summary
This paper talks about X-Dyna, a new AI system that can take a single photo of a person and make it move realistically, copying facial expressions and body movements from a video. It's like bringing a still picture to life, making it look natural and lifelike.
What's the problem?
Current methods for animating still images of people often lose important details, making the results look fake or unnatural. They struggle to capture small movements and expressions that make animations look real, especially when it comes to facial expressions and the surrounding environment. It's like trying to make a puppet move like a real person - it's hard to get all the little details right.
What's the solution?
The researchers created X-Dyna, which uses a special AI technique called diffusion. They added two key parts to make it work better: a Dynamics-Adapter, which helps keep the person's appearance consistent while allowing for smooth, detailed movements, and a local control module that focuses on facial expressions. X-Dyna learns from watching lots of videos of people moving and scenes with natural movement, helping it create more realistic animations. It's like teaching the AI to be a really good puppeteer who can make the still image move just like a real person would.
Why it matters?
This matters because it brings us closer to creating lifelike digital humans from just a single photo. It could be used in movies, video games, or virtual reality to make more realistic characters without needing to film real actors for every scene. It also shows how AI is getting better at understanding and recreating human movement, which could lead to advances in fields like computer graphics, animation, and even robotics. For everyday people, it might mean being able to bring old photos to life or create fun, animated versions of themselves for social media. Overall, X-Dyna is pushing the boundaries of what's possible in digital animation and human-computer interaction.
Abstract
We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment. Building on prior approaches centered on human pose control, X-Dyna addresses key shortcomings causing the loss of dynamic details, enhancing the lifelike qualities of human video animations. At the core of our approach is the Dynamics-Adapter, a lightweight module that effectively integrates reference appearance context into the spatial attentions of the diffusion backbone while preserving the capacity of motion modules in synthesizing fluid and intricate dynamic details. Beyond body pose control, we connect a local control module with our model to capture identity-disentangled facial expressions, facilitating accurate expression transfer for enhanced realism in animated scenes. Together, these components form a unified framework capable of learning physical human motion and natural scene dynamics from a diverse blend of human and scene videos. Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna outperforms state-of-the-art methods, creating highly lifelike and expressive animations. The code is available at https://github.com/bytedance/X-Dyna.