X-Dyna: Expressive Dynamic Human Image Animation
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
HyperMotion: DiT-Based Pose-Guided Human Image Animation of Complex Motions
MTVCrafter: 4D Motion Tokenization for Open-World Human Image Animation
TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection
UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape Images
DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.