This work presents AnyDoor, a diffusion-based image generator with the power
to teleport target objects to new scenes at user-specified locations in a
harmonious way. Instead of tuning parameters for each object, our model is
trained only once and effortlessly generalizes to diverse object-scene
combinations at the inference stage. Such a challenging zero-shot setting
requires an adequate characterization of a certain object. To this end, we
complement the commonly used identity feature with detail features, which are
carefully designed to maintain texture details yet allow versatile local
variations (e.g., lighting, orientation, posture, etc.), supporting the object
in favorably blending with different surroundings. We further propose to borrow
knowledge from video datasets, where we can observe various forms (i.e., along
the time axis) of a single object, leading to stronger model generalizability
and robustness. Extensive experiments demonstrate the superiority of our
approach over existing alternatives as well as its great potential in
real-world applications, such as virtual try-on and object moving. Project page
is this https URL

AnyDoor 是一种基于扩散的图像生成器，它能够以和谐的方式将目标对象传送到用户指定的位置。通过使用细节特征来补充身份特征，该模型能够在推理阶段对不同的对象 - 场景组合进行普适化。通过借鉴视频数据集的知识，该方法在现实世界的虚拟试穿和物体移动等应用中展现出优越性和巨大潜力。