PixTex: Consistent 3D Texturing via Pixel-Space Multi-View Diffusion

Abstract

Existing texture generation methods rely heavily on latent diffusion models, whose VAE-based spatial compression inherently limits fine-grained detail preservation and degrades pixel-level multi-view consistency. To address this limitation, we introduce PixTex, the first pixel-space multi-view diffusion framework for texture generation, which achieves substantially improved multi-view consistency. Operating directly in image space avoids latent compression, reduces inconsistencies introduced during latent-to-RGB upsampling, and preserves lossless pixel-level geometric guidance for accurate multi-view consistency. However, directly applying pixel-wise attention across multiple views is computationally prohibitive. To balance efficiency and fidelity, we adopt a coarse-to-fine consistency strategy: i) At a coarse patch level, we establish cross-view structural correspondence by employing 5D RoPE to correlate 2D patch coordinates with 3D world-space positions. ii) At the pixel level, a specialized 3D position-aware detailer further refines textural details based on patch features, ensuring fine-grained alignment unattainable by VAE-based methods. Additionally, we propose a novel consistency loss to explicitly guarantee multi-view coherence. Finally, we incorporate a pixel-space multi-view inpainting module to resolve self-occlusions and improve texture completeness. Extensive experiments demonstrate that our framework achieves state-of-the-art multi-view consistency, producing high-fidelity and seamless textures.