Direct3D-V3: Latent-Free Diffusion Transformers in the Native Voxel Domain
Year: 2026
Type: article
Abstract: Native 3D generation has largely been built around latent diffusion, with recent advances concentrating on stronger variational autoencoders (VAE) and increasingly elaborate latent representations, from vecset and triplanes to structured sparse voxels. Despite their architectural differences, these methods all share the same premise: geometry is first compressed into a latent space and only then modeled by the generator. This paradigm carries two intrinsic limitations. First, VAE reconstruction removes fine geometric detail before the diffusion model ever sees the data. Second, the two-stage pipeline creates an objective mismatch, since the VAE is trained for reconstruction whereas the diffusion model is trained for distribution matching. We take a different route and propose Direct3D-V3, a latent-free diffusion transformer (DiT) framework that discards the VAE altogether and operates in the native voxel domain, directly modeling raw SDF voxel grids during both training and sampling. To make this direct voxel formulation geometrically reliable, we introduce an implicit manifold regularization over neighboring voxels to suppress non-watertight surfaces, and convert predicted SDF grids into meshes via Marching Cubes so that rendered normal losses can supervise the model directly from its predicted SDF. To improve alignment between the conditioning image and generated geometry, Direct3D-V3 also predicts the camera parameters of the conditioning image as an auxiliary task, encouraging the DiT to learn the mapping from image observations to 3D shape. This latent-free design removes the reconstruction bottleneck, brings training under an objective in the native voxel domain, and yields meshes with substantially richer local detail. The predicted camera and native SDF voxel output further make Direct3D-V3 extensible to generative reconstruction, where diffusion is interleaved with normal-guided refinement to improve alignment with the observed view while the generative prior completes unseen regions. Extensive experiments show that Direct3D-V3 achieves competitive results to state-of-the-art latent-based methods, while producing finer geometry.
Source: ACM Transactions on Graphics
DOI: 10.1145/3842524
Institutions: Nanjing University, University of Hong Kong, University of Oxford
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7225646265