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

TL;DR

REVIVE 3D introduces a two-stage pipeline that enhances flat images into voluminous 3D assets by constructing an Inflated Prior and refining 3D latent representations, achieving state-of-the-art results.

Contribution

The paper presents a novel two-stage, plug-and-play framework for generating detailed, voluminous 3D assets from flat images, incorporating new metrics for volume and surface quality assessment.

Findings

REVIVE 3D outperforms existing methods on flat image datasets.

Proposed metrics correlate well with human perception of volume and quality.

Framework supports image-conditioned 3D editing.

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 assets when the input is a flat image that provides limited 3D cues. We introduce REVIVE 3D, a two-stage, plug-and-play pipeline for generating voluminous 3D assets from flat images. In Stage 1, we construct an Inflated Prior by inflating the foreground silhouette to recover global volume and superimposing part-aware details to capture local structure. In Stage 2, 3D Latent Refinement injects Gaussian noise into the Inflated Prior's latent and then denoises it, using the prior's geometric cues to leverage the backbone's pretrained 3D knowledge. Furthermore, our framework supports image-conditioned 3D editing. To quantify volume and surface flatness, we propose Compactness and Normal Anisotropy. We validate Compactness and Normal Anisotropy through a user study, showing that these metrics align with human perception of volume and quality. We show that REVIVE 3D achieves state-of-the-art performance on a challenging flat image dataset, based on extensive qualitative and quantitative evaluations.