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REVIVE 3D generates voluminous 3D assets from flat images with novel enhancement pipeline

Researchers have developed REVIVE 3D, a novel two-stage pipeline designed to generate detailed 3D assets from flat 2D images. The system first creates an "Inflated Prior" by recovering global volume and adding part-aware details, then refines this prior using a latent diffusion process. This approach aims to overcome limitations in current generative models that struggle with volumetric output from limited 3D cues. The framework also supports image-conditioned 3D editing and introduces new metrics, Compactness and Normal Anisotropy, to evaluate volume and surface quality. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new method for generating detailed 3D assets from 2D images, potentially improving content creation pipelines.

RANK_REASON This is a research paper detailing a new method for 3D asset generation.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Hankyeol Lee, Wooyeol Baek, Seongdo Kim, Jongyoo Kim ·

    REVIVE 3D: Refinement via Encoded Voluminous Inflated prior for Volume Enhancement

    arXiv:2604.27504v1 Announce Type: new Abstract: Recent generative models have shown strong performance in generating diverse 3D assets from 2D images, a fundamental research topic in computer vision and graphics. However, these models still struggle to generate voluminous 3D asse…

  2. arXiv cs.CV TIER_1 · Jongyoo Kim ·

    REVIVE 3D: Refinement via Encoded Voluminous Inflated prior for Volume Enhancement

    Recent generative models have shown strong performance in generating diverse 3D assets from 2D images, a fundamental research topic in computer vision and graphics. However, these models still struggle to generate voluminous 3D assets when the input is a flat image that provides …