DSTex: Dense Surface Decoding for Native PBR Texture Generation

Given a mesh and a reference image, native physically based rendering (PBR) texture generation synthesizes relightable surface materials directly in 3D. Latent-based frameworks typically compress these materials with an autoencoder and learn to generate the resulting latents through diffusion. Reconstructing fine lettering and intricate patterns from compact latents remains challenging, while increasing their number raises diffusion costs during training and sampling. We present DSTex, a native PBR texture generation framework that improves reconstruction by expanding the decoder's spatial feature capacity at a fixed latent count. Its autoencoder, DS-VAE, encodes material point samples into compact anchor tokens and expands them at finer locations sampled from the known mesh. Shared feature projection and relative-position modulation adapt each latent's content to these locations, constructing fine anchor tokens for continuous material queries without adding diffusion tokens. An image- and geometry-conditioned rectified-flow model generates the compact latents, which DS-VAE decodes into PBR materials on the input mesh. Quantitative and qualitative evaluations demonstrate that DSTex improves material reconstruction at matched latent counts and outperforms the evaluated open-source baselines in PBR texture generation.