9 citations · 15 across the 14 of their papers we have counts for
18 papers
Position Forcing: Self-Conditioning 3D Generation
Ziheng Ouyang, Zeqiang Lai, Jiarui Chen +7
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…
MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers
Jiarui Chen, Zeqiang Lai, Jiangshan Wang +5
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. H…
Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?
Jiangshan Wang, Zeqiang Lai, Jiayi Guo +6
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 m…
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…
PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation
Chunshi Wang, Haohan Weng, Junliang Ye +9
Autoregressive Transformers dominate high-quality mesh generation by producing artist-worthy topologies, yet their inherent sequential decoding induces substantial computational ov…
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…
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…
LATTICE: Democratize High-Fidelity 3D Generation at Scale
Zeqiang Lai, Yunfei Zhao, Zibo Zhao +5
We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis…
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…
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…
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…
X-Part: high fidelity and structure coherent shape decomposition
Xinhao Yan, Jiachen Xu, Yang Li +8
Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods ofte…
Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
Zeqiang Lai, Yunfei Zhao, Haolin Liu +23
In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages…
Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
Team Hunyuan3D, Shuhui Yang, Mingxin Yang +50
3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several gr…
Unleashing Vecset Diffusion Model for Fast Shape Generation
Zeqiang Lai, Yunfei Zhao, Zibo Zhao +10
3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vecset Diffusion Model (VDM). While recent advanceme…
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
Zibo Zhao, Zeqiang Lai, Qingxiang Lin +71
We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-sca…
Scaling Mesh Generation via Compressive Tokenization
Haohan Weng, Zibo Zhao, Biwen Lei +10
We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh…
FlexiTex: Enhancing Texture Generation via Visual Guidance
DaDong Jiang, Xianghui Yang, Zibo Zhao +7
Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models. However, abstract t…