🔥 Native and Compact Structured Latents for 3D Generation

💡 This paper addresses the challenge of 3D generative modeling where existing representations struggle to capture complex topologies and detailed appearance of 3D assets. To overcome this, the authors introduce a new sparse voxel representation called O-Voxel, which encodes both geometry and appearance of 3D objects. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, and captures comprehensive surface attributes. The authors design a Sparse Compression VAE based on O-Voxel, which provides a high spatial compression rate and a compact latent space. They train large-scale models with 4B parameters on diverse public 3D asset datasets and achieve highly efficient inference. The results show that the generated assets have significantly better geometry and material quality compared to existing models. The approach offers a significant advancement in 3D generative modeling by enabling high-quality generation with efficient inference and robust topology handling.

📅 Published on Dec 16, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2512.14692
• PDF: https://arxiv.org/pdf/2512.14692
• Project Page: https://microsoft.github.io/TRELLIS.2/

🤖 Models citing this paper:
• https://huggingface.co/microsoft/TRELLIS.2-4B
• https://huggingface.co/mancub/TRELLIS.2-4B
• https://huggingface.co/Jinstudio/TRELLIS.2-4B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/serpentine-b/t2

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/microsoft/TRELLIS.2
• https://huggingface.co/spaces/TencentARC/Pixal3D
• https://huggingface.co/spaces/broyang/3dai

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📢 By: https://xn--r1a.website/PaperNexus

#3DGenerativeModeling #SparseVoxelRepresentation #CompactLatentSpace #3DAssetGeneration #GeometricDeepLearning