Spatial and Surface Correspondence Field for Interaction Transfer
arxiv(2024)
摘要
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.
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