Author

Zi-Juan Ouyang

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Preprint Sep 2026

Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?

Native 3D texture generation synthesizes colors directly in 3D space for a given geometry, conditioned on multi-view reference images. It is generally believed that training such models requires large-scale, high-quality real 3D asset data, whose acquisition remains a long-standing and challenging problem. In this work...

Jiang-Shan Wang, Ze-Qiang Lai, J. Guo et al. · 2 citations

Preprint Oct 2026

Position Forcing: Self-Conditioning 3D Generation

Recent single-stage 3D generative models commonly adopt VecSet representations, encoding 3D shapes as unordered sets of latent tokens. However, compared with two-stage methods that provide explicit positional guidance, these models must implicitly infer token positions throughout denoising, limiting their generation qu...

Zi-Juan Ouyang, Ze-Qiang Lai, Jia-Rui Chen et al. · 0 citations

#artificial intelligence Preprint Oct 2026

MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers

Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resolution 3D asset generation. However, existing methods can degrade generation quality and fidelity at high sparsity levels. Through controlled oracle comparisons, we trac...

Jia-Rui Chen, Ze-Qiang Lai, Jiang-Shan Wang et al. · 0 citations