InnerScene: A 4D Hand-object Interaction Dataset for Densely Packed Inner Storage Scenes

3D scene data increasingly support perception, generation, and manipulation, yet densely stocked inner-container scenes remain difficult to capture with the geometry and action history these tasks jointly require. A stocked cabinet is the result of many placements, but its final state hides the insertion paths through shrinking free space that produced it; walls and clutter conceal even the objects inside. Directly synthesizing this linked 4D record is difficult because each placement changes the geometry and demands plausible hand–object contact. We introduce InnerScene, to our knowledge the first large-scale, real-captured 4D dataset of densely packed inner-container scenes linking reconstructed object assets and metric layouts to synchronized multi-view observations and estimated hand–object placement trajectories. We record placements progressively in an open capture setup that preserves container spatial constraints, then register objects and motion in a shared metric frame. The resulting data support three practical uses: conditional layout generation populates virtual cabinets with specified objects for retrieval and rearrangement research; single-view instance-level reconstruction turns a cabinet-opening image into a 3D inventory for reasoning about access; and human-to-robot trajectory transfer turns selected recorded insertions into executed robot placements in crowded cabinets. InnerScene thus ties what is stored, where it is stored, and how it was placed into one resource for building, understanding, and acting in inner-container scenes.