Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion

High-resolution 3D asset generation is vital in various 3D ap-plications. Existing state-of-the-art diffusion-based models remain con-strained by fixed resolutions, limiting their ability to produce details. Inthis paper, we tackle the challenge of generating more detailed, higher-resolution 3D objects by introducing a 3D super-resolution (SR) frame-work built on existing 3D generative foundation models. To this end,we design PLSR, a progressive and localized super-resolution solutionto achieve this goal effectively and memory efficiently. Technically, givena coarse geometry from a pretrained 3D generator, we decompose theglobal SR task into localized sub-tasks via an associative input de-composition scheme, adapt a flow-based 3D generator into a local-ized super-resolution model through low-cost finetuning, and unifythem in an iterative patch-wise denoising pipeline for seamless high-resolution output. Experiments on challenging objects show that our ap-proach is able to generate 3D details with new strong fine-detail fidelitywhile significantly reducing the computational cost, offering a new andpractical solution for high-resolution 3D asset generation.