Direct 3D-Aware Object Insertion via Decomposed Visual Proxies

2026-06-04

AI Summary

Existing diffusion-based object insertion methods treat the task solely as 2D image inpainting, lacking explicit control over the 3D pose of inserted objects. This work proposes DIRECT, a novel framework that, for the first time, integrates user-controllable 3D proxies with 2D diffusion generation. By decoupling appearance, geometry, and contextual guidance signals and injecting them into separate pathways, DIRECT effectively mitigates feature entanglement. The approach enables precise 3D pose control and scene-adaptive placement while preserving the visual fidelity of reference objects. Experimental results demonstrate that DIRECT outperforms existing methods in both geometric controllability and visual quality, supporting high-fidelity object insertion under interactive 3D pose adjustments.

Abstract

Object insertion aims to seamlessly composite a reference object into a specified region of a background image. Recent diffusion-based methods achieve high visual quality but formulate insertion as a simple 2D inpainting task, providing no explicit control over the object's 3D pose and limiting their practical applicability. We propose DIRECT (Decomposed Injection for Reference Composition and Target-integration), a novel framework that integrates interactive pose manipulation with high-fidelity 2D image synthesis to enable pose-controllable object insertion. Our method decomposes the insertion conditions into three complementary components: appearance guidance capturing visual details from the reference object, geometry guidance derived from the user-adjusted 3D proxy, and context guidance from the target background. By injecting them through separate pathways, DIRECT avoids feature entanglement and simultaneously preserves reference appearance, follows the user-specified pose, and adapts the object to the target scene. We also introduce an automated data construction pipeline to improve the diversity and quality of training data. Experiments show that DIRECT outperforms previous methods in both geometric controllability and visual quality.

Problem

• object insertion
• 3D pose control
• diffusion models
• image composition
• visual proxies

Innovation

• 3D-aware object insertion
• pose-controllable synthesis
• decomposed visual proxies
• diffusion models
• interactive 3D manipulation