DreamArt: Generating Interactable Articulated Objects from a Single Image

TL;DR

DreamArt is a new framework that creates high-quality, interactable articulated 3D objects from a single image, combining segmentation, articulation modeling, and texture refinement for scalable asset generation.

Contribution

It introduces a three-stage pipeline for generating articulated 3D objects from single images, including part segmentation, articulation prior learning, and texture optimization, which is novel in this domain.

Findings

Produces high-fidelity, articulated 3D objects from single images

Accurately models part shapes and plausible articulation

Ensures coherent textures across object parts

Abstract

Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods primarily focus on surface geometry and texture, neglecting part decomposition and articulation modeling. Meanwhile, neural reconstruction approaches (e.g., NeRF or Gaussian Splatting) rely on dense multi-view or interaction data, limiting their scalability. In this paper, we introduce DreamArt, a novel framework for generating high-fidelity, interactable articulated assets from single-view images. DreamArt employs a three-stage pipeline: firstly, it reconstructs part-segmented and complete 3D object meshes through a combination of image-to-3D generation, mask-prompted 3D segmentation, and part amodal completion. Second, we fine-tune a video diffusion model to capture part-level articulation priors, leveraging movable part masks as prompt and amodal images to mitigate ambiguities caused by occlusion. Finally, DreamArt optimizes the articulation motion, represented by a dual quaternion, and conducts global texture refinement and repainting to ensure coherent, high-quality textures across all parts. Experimental results demonstrate that DreamArt effectively generates high-quality articulated objects, possessing accurate part shape, high appearance fidelity, and plausible articulation, thereby providing a scalable solution for articulated asset generation. Our project page is available at https://dream-art-0.github.io/DreamArt/.