InsideArt: Benchmarking Articulated 3D Generation with Usable Interiors

Usable articulated containers require accessible interior space, not just accurate exterior geometry and joints. We introduce InsideArt, a dataset and benchmark linking articulated 3D containers to support surfaces, interior free space, ordered and alternative access paths, and language-described item arrangements. Our data-construction pipeline combines vision–language proposals, human verification, geometric constraints, and rigid-body simulation to produce over 100,000 placement scenes across 654 containers with 1,981 annotated free-space regions. The dataset supports three complementary tasks: image-conditioned articulated container generation, language-guided interior synthesis, and language-guided item placement. We evaluate interior-volume recovery and access relations beyond surface similarity, and define a placement protocol covering containment, collision freedom, stability, and orientation compliance. Reconstruction and free-space evaluations reveal that accurate surfaces can coexist with missing interior regions and incorrect access relations, highlighting the need to evaluate functionality beyond appearance and joint accuracy.