2 citations · 3 across the 7 of their papers we have counts for
10 papers
Seg3DParts: Segmentation-Grounded Controllable Part-Level 3D Generation
Jiantao Lin, Meixi Chen, Yingjie Xu +5
Part-level 3D assets are essential for editing, reassembly, and interaction, yet recovering such structure from a single image remains challenging due to occlusion, ambiguous bound…
FlexPainter: Flexible and Multi-View Consistent Texture Generation
Dongyu Yan, Leyi Wu, Jiantao Lin +7
Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. How…
Advancing high-fidelity 3D and Texture Generation with 2.5D latents
Xin Yang, Jiantao Lin, Yingjie Xu +2
Despite the availability of large-scale 3D datasets and advancements in 3D generative models, the complexity and uneven quality of 3D geometry and texture data continue to hinder t…
DiMeR: Disentangled Mesh Reconstruction Model
Lutao Jiang, Jiantao Lin, Kanghao Chen +7
We propose DiMeR, a novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction. Existing methods confront two persistent obstacl…
ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and Reactive Feedback
Litao Guo, Xinli Xu, Luozhou Wang +5
With the rapid advancement of generative models, general-purpose generation has gained increasing attention as a promising approach to unify diverse tasks across modalities within…
Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
Jiantao Lin, Xin Yang, Meixi Chen +7
Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods of…
PRM: Photometric Stereo based Large Reconstruction Model
Wenhang Ge, Jiantao Lin, Guibao Shen +4
We propose PRM, a novel photometric stereo based large reconstruction model to reconstruct high-quality meshes with fine-grained local details. Unlike previous large reconstruction…
FlexGen: Flexible Multi-View Generation from Text and Image Inputs
Xinli Xu, Wenhang Ge, Jiantao Lin +5
In this work, we introduce FlexGen, a flexible framework designed to generate controllable and consistent multi-view images, conditioned on a single-view image, or a text prompt, o…
SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance
Guibao Shen, Luozhou Wang, Jiantao Lin +9
Recent advancements in text-to-image generation have been propelled by the development of diffusion models and multi-modality learning. However, since text is typically represented…
LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching
Yixun Liang, Xin Yang, Jiantao Lin +3
The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various re…