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20242026
most citedHunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

9 citations · 15 across the 14 of their papers we have counts for

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11 papers · 1 filter

cs.CV2026

Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling

Kaiyi Zhang, Zhihao Liang, Haolin Liu +8

Sparse voxel grids preserve the spatial structure needed for detailed 3D reconstruction, but their memory still grows rapidly with resolution as active surface cells increase. We i…

cs.CV2026

Elastic Diffusion Transformer

Jiangshan Wang, Zeqiang Lai, Jiarui Chen +5

Diffusion Transformers (DiT) have demonstrated remarkable generative capabilities but remain highly computationally expensive. Previous acceleration methods, such as pruning and di…

cs.CV2026

HY3D-Bench: Generation of 3D Assets

Team Hunyuan3D, :, Bowen Zhang +22

While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlene…

cs.CV2025★ 1 cited

NaTex: Seamless Texture Generation as Latent Color Diffusion

Zeqiang Lai, Yunfei Zhao, Zibo Zhao +5

We present NaTex, a native texture generation framework that predicts texture color directly in 3D space. In contrast to previous approaches that rely on baking 2D multi-view image…

cs.CV2025

Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation

Weimin Bai, Yubo Li, Weijian Luo +4

Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations…

cs.CV2025★ 1 cited

Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets

Team Hunyuan3D, :, Bowen Zhang +17

Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning…