9 citations · 15 across the 26 of their papers we have counts for
12 papers · 1 filter
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…
FlowHMR: Physically Plausible Motion Capture from Video
Zhanke Wang, Chengfeng Zhao, Qing Shuai +8
We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typically regress human motion dire…
AESOP: Asymmetric Human-Camera Generation with Translation-Intensity Control
Jingzhong Lin, Zhanke Wang, Heng Li +9
Human motion defines an action, while a camera trajectory determines how it is presented. Camera generation for a given human motion and joint human-camera generation are usually t…
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…
AnimateCanvas: Learning Implicit Motion Planning from Composable Kinematic Cues
Zeyu Ling, Di Kang, Qing Shuai +7
Professional character animation requires both natural motion and precise, versatile control. For example, creators often define the timing of a specified action, control the motio…
Flow3D-OPD: Multi-Teacher On-Policy Distillation for 3D Geometry Generation with Flow-Matching Diffusion Transformer
Zhiwei Ning, Zhen Zhou, Puhua Jiang +7
Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training strategy remains largely unexplor…
Hyperbolic Hierarchical Clustering for Visual Representation Learning
Jianan Wei, Guikun Chen, Zhiyuan Weng +3
We investigate the token mixer in vision backbones by revisiting clustering, one of the most classic approaches in machine learning. An effective token mixer is a fundamental compo…
Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing
Junliang Ye, Kenkun Liu, Guocun Wang +13
Recent advances in image generation have demonstrated the potential of unified multimodal models that integrate understanding, generation, and editing. However, unified 3D modeling…
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…
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…