TIMI: Training-Free Image-to-3D Multi-Instance Generation with Spatial Fidelity

TL;DR

TIMI is a training-free framework that enhances image-to-3D multi-instance generation by leveraging pre-trained models' spatial priors, ensuring high spatial fidelity without additional training.

Contribution

The paper introduces TIMI, a novel training-free approach with modules ISG and SGU that improve spatial fidelity and instance disentanglement in image-to-3D generation.

Findings

Outperforms existing methods in global layout accuracy

Achieves better local instance separation

Operates without additional training, enabling faster inference

Abstract

Precise spatial fidelity in Image-to-3D multi-instance generation is critical for downstream real-world applications. Recent work attempts to address this by fine-tuning pre-trained Image-to-3D (I23D) models on multi-instance datasets, which incurs substantial training overhead and struggles to guarantee spatial fidelity. In fact, we observe that pre-trained I23D models already possess meaningful spatial priors, which remain underutilized as evidenced by instance entanglement issues. Motivated by this, we propose TIMI, a novel Training-free framework for Image-to-3D Multi-Instance generation that achieves high spatial fidelity. Specifically, we first introduce an Instance-aware Separation Guidance (ISG) module, which facilitates instance disentanglement during the early denoising stage. Next, to stabilize the guidance introduced by ISG, we devise a Spatial-stabilized Geometry-adaptive Update (SGU) module that promotes the preservation of the geometric characteristics of instances while maintaining their relative relationships. Extensive experiments demonstrate that our method yields better performance in terms of both global layout and distinct local instances compared to existing multi-instance methods, without requiring additional training and with faster inference speed.