In this paper, we introduce a new method for the task of interaction
transfer. Given an example interaction between a source object and an agent,
our method can automatically infer both surface and spatial relationships for
the agent and target objects within the same category, yielding more accurate
and valid transfers. Specifically, our method characterizes the example
interaction using a combined spatial and surface representation. We correspond
the agent points and object points related to the representation to the target
object space using a learned spatial and surface correspondence field, which
represents objects as deformed and rotated signed distance fields. With the
corresponded points, an optimization is performed under the constraints of our
spatial and surface interaction representation and additional regularization.
Experiments conducted on human-chair and hand-mug interaction transfer tasks
show that our approach can handle larger geometry and topology variations
between source and target shapes, significantly outperforming state-of-the-art
methods.

本文介绍了一种新的交互转移方法，能够自动推断源对象和代理人之间的表面和空间关系，并以更准确、有效的方式对目标对象进行转移，通过结合空间和表面表示来描述示例交互，利用学得的空间和表面对应场将与表示相关的代理点和对象点对应到目标对象空间，在我们的空间和表面交互表示约束和附加正则化的约束下进行优化，实验在人与椅子以及手与杯子的交互转移任务上表明，我们的方法可以处理源形状和目标形状之间较大的几何和拓扑变化，明显优于现有方法。

交互迁移的空间和表面对应场

Spatial and Surface Correspondence Field for Interaction Transfer

Unsupervised image-to-image translation aims at learning a mapping between
two visual domains. However, learning a translation across large geometry
variations always ends up with failure. In this work, we present a novel
disentangle-and-translate framework to tackle the complex objects
image-to-image translation task. Instead of learning the mapping on the image
space directly, we disentangle image space into a Cartesian product of the
appearance and the geometry latent spaces. Specifically, we first introduce a
geometry prior loss and a conditional VAE loss to encourage the network to
learn independent but complementary representations. The translation is then
built on appearance and geometry space separately. Extensive experiments
demonstrate the superior performance of our method to other state-of-the-art
approaches, especially in the challenging near-rigid and non-rigid objects
translation tasks. In addition, by taking different exemplars as the appearance
references, our method also supports multimodal translation. Project page:
this https URL

本文采用解缠和翻译框架解决复杂物体的图像翻译任务，使用外观和几何空间提取出独立但互补的表示，并分开翻译。实验结果表明，该方法在接近刚性和非刚性物体翻译任务中的表现优于其他最先进方法，同时支持多模式翻译。