Rigged objects are commonly used in artist pipelines, as they can flexibly adapt to different scenes and postures. However, articulating the rigs into realistic affordance-aware postures (e.g., following the context, respecting the physics and the personalities of the object) remains time-consuming and heavily relies on human labor from experienced artists. In this paper, we tackle the novel problem and design A3Syn. With a given context, such as the environment mesh and a text prompt of the desired posture, A3Syn synthesizes articulation parameters for arbitrary and open-domain rigged objects obtained from the Internet. The task is incredibly challenging due to the lack of training data, and we do not make any topological assumptions about the open-domain rigs. We propose using 2D inpainting diffusion model and several control techniques to synthesize in-context affordance information. Then, we develop an efficient bone correspondence alignment using a combination of differentiable rendering and semantic correspondence. A3Syn has stable convergence, completes in minutes, and synthesizes plausible affordance on different combinations of in-the-wild object rigs and scenes.
@article{arxiv.2501.12393,
title = {Towards Affordance-Aware Articulation Synthesis for Rigged Objects},
author = {Yu-Chu Yu and Chieh Hubert Lin and Hsin-Ying Lee and Chaoyang Wang and Yu-Chiang Frank Wang and Ming-Hsuan Yang},
journal= {arXiv preprint arXiv:2501.12393},
year = {2025}
}