GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models
This addresses the gap in text-conditioned generation for articulated objects, which is incremental as it builds on existing diffusion and graph-based methods.
The paper tackles the problem of generating articulated 3D objects from text prompts, proposing GAOT, a framework that uses diffusion models and hypergraph learning to achieve superior performance over previous methods on the PartNet-Mobility dataset.
Articulated object generation has seen increasing advancements, yet existing models often lack the ability to be conditioned on text prompts. To address the significant gap between textual descriptions and 3D articulated object representations, we propose GAOT, a three-phase framework that generates articulated objects from text prompts, leveraging diffusion models and hypergraph learning in a three-step process. First, we fine-tune a point cloud generation model to produce a coarse representation of objects from text prompts. Given the inherent connection between articulated objects and graph structures, we design a hypergraph-based learning method to refine these coarse representations, representing object parts as graph vertices. Finally, leveraging a diffusion model, the joints of articulated objects-represented as graph edges-are generated based on the object parts. Extensive qualitative and quantitative experiments on the PartNet-Mobility dataset demonstrate the effectiveness of our approach, achieving superior performance over previous methods.