8 papers
Enhancing In-context Panoramic Generation via Geometric-aware Pretraining
Haoran Feng, Ruiyang Zhang, Longyi Zhang +2
In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream task-specific fine-tuning. To…
PanoWorld: Real-World Panoramic Generation
Haoyuan Li, Dizhe Zhang, Yuemei Zhou +7
In this work, we aim to address the challenge of long-range memory in panoramic world models by exploiting the rotation-equivariant property of omnidirectional representations, whe…
Repurposing 3D Generative Model for Autoregressive Layout Generation
Haoran Feng, Yifan Niu, Zehuan Huang +3
We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from textual descriptions, LaviGe…
SegviGen: Repurposing 3D Generative Model for Part Segmentation
Lin Li, Haoran Feng, Zehuan Huang +8
We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or…
DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training
Haoran Feng, Dizhe Zhang, Xiangtai Li +2
In this work, we propose DiT360, a DiT-based framework that performs hybrid training on perspective and panoramic data for panoramic image generation. For the issues of maintaining…
VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
Lin Li, Zehuan Huang, Haoran Feng +4
3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, b…
AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models
Zehuan Huang, Haoran Feng, Yangtian Sun +3
We present AnimaX, a feed-forward 3D animation framework that bridges the motion priors of video diffusion models with the controllable structure of skeleton-based animation. Tradi…
Personalize Anything for Free with Diffusion Transformer
Haoran Feng, Zehuan Huang, Lin Li +2
Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing. Recent training-free approaches, while exhibit higher computational…