Problem
Research questions and friction points this paper is trying to address.
3D Super-Resolution
High-Resolution 3D Objects
Diffusion-based Models
Fixed Resolution Limitation
PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion
🤖 AI Summary
本文提出PLSR方法，通过局部潜伏体扩散解决3D对象高分辨率生成问题，提高了细节保真度并降低了计算成本。
📝 Abstract
High-resolution 3D asset generation is vital in various 3D applications. Existing state-of-the-art diffusion-based models remain constrained by fixed resolutions, limiting their ability to produce details. In this paper, we tackle the challenge of generating more detailed, higher-resolution 3D objects by introducing a 3D super-resolution (SR) framework built on existing 3D generative foundation models. To this end, we design PLSR, a progressive and localized super-resolution solution to achieve this goal effectively and memory efficiently. Technically, given a coarse geometry from a pretrained 3D generator, we decompose the global SR task into localized sub-tasks via an associative input decomposition scheme, adapt a flow-based 3D generator into a localized super-resolution model through low-cost finetuning, and unify them in an iterative patch-wise denoising pipeline for seamless high-resolution output. Experiments on challenging objects show that our approach is able to generate 3D details with new strong fine-detail fidelity while significantly reducing the computational cost, offering a new and practical solution for high-resolution 3D asset generation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Progressive and Localized Super-Resolution
Localized Latent Voxel Diffusion
3D Generative Models
Patch-wise Denoising Pipeline
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