Pixal3D: Pixel-Aligned 3D Generation from Images

2026-05-11

AI Summary

Existing 3D generative models often struggle to achieve pixel-level fidelity when synthesizing 3D assets from images due to ambiguities in 2D–3D correspondences. This work proposes a pixel-aligned 3D generation paradigm that explicitly lifts multi-scale 2D image features into a 3D feature volume consistent with the input viewpoint via a pixel reprojection mechanism, integrated within an end-to-end trainable, native 3D generative model. The approach achieves, for the first time, large-scale, natively 3D pixel-aligned synthesis, significantly improving geometric and appearance fidelity under both single-image and multi-view settings—approaching the quality of traditional reconstruction methods—and successfully extends to high-fidelity, object-disentangled scene-level synthesis tasks.

Abstract

Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appearance. Yet fidelity, which measures pixel-level faithfulness of the generated 3D asset to the input image, still remains a central bottleneck. We argue this stems from an implicit 2D-3D correspondence issue: most 3D-native generators synthesize shape in canonical space and inject image cues via attention, leaving pixel-to-3D associations ambiguous. To tackle this issue, we draw inspiration from 3D reconstruction and propose Pixal3D, a pixel-aligned 3D generation paradigm for high-fidelity 3D asset creation from images. Instead of generating in a canonical pose, Pixal3D directly generates 3D in a pixel-aligned way, consistent with the input view. To enable this, we introduce a pixel back-projection conditioning scheme that explicitly lifts multi-scale image features into a 3D feature volume, establishing direct pixel-to-3D correspondence without ambiguity. We show that Pixal3D is not only scalable and capable of producing high-quality 3D assets, but also substantially improves fidelity, approaching the fidelity level of reconstruction. Furthermore, Pixal3D naturally extends to multi-view generation by aggregating back-projected feature volumes across views. Finally, we show pixel-aligned generation benefits scene synthesis, and present a modular pipeline that produces high-fidelity, object-separated 3D scenes from images. Pixal3D for the first time demonstrates 3D-native pixel-aligned generation at scale, and provides a new inspiring way towards high-fidelity 3D generation of object or scene from single or multi-view images. Project page: https://ldyang694.github.io/projects/pixal3d/

Problem

• fidelity
• 2D-3D correspondence
• image-to-3D synthesis
• pixel-aligned generation
• 3D generative models

Innovation

• pixel-aligned generation
• 3D generative model
• pixel-to-3D correspondence
• back-projection conditioning
• high-fidelity 3D synthesis