3 citations · 5 across the 18 of their papers we have counts for
19 papers
Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction
Yang-Tian Sun, Tianjia Liu, Zehuan Huang +7
Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challenge. A central difficulty lie…
Learning 3D Editing without Paired Supervision via Generative Prior Distillation
Hao Wen, Weibin Yun, Hongxing Fan +4
Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality paired training data. Existi…
PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World
Yunhan Yang, Chunshi Wang, Junliang Ye +7
Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlookin…
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…
Stereo World Model: Camera-Guided Stereo Video Generation
Yang-Tian Sun, Zehuan Huang, Yifan Niu +4
We present StereoWorld, a camera-conditioned stereo world model that jointly learns appearance and binocular geometry for end-to-end stereo video generation.Unlike monocular RGB or…
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…
MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation
Zhiqi Li, Wenhuan Li, Tengfei Wang +8
Compositionality is critical for 3D object and scene generation, but existing part-aware 3D generation methods suffer from poor scalability due to quadratic global attention costs…
InterMoE: Individual-Specific 3D Human Interaction Generation via Dynamic Temporal-Selective MoE
Lipeng Wang, Hongxing Fan, Haohua Chen +2
Generating high-quality human interactions holds significant value for applications like virtual reality and robotics. However, existing methods often fail to preserve unique indiv…
Multi-Agent Amodal Completion: Direct Synthesis with Fine-Grained Semantic Guidance
Hongxing Fan, Lipeng Wang, Haohua Chen +3
Amodal completion, generating invisible parts of occluded objects, is vital for applications like image editing and AR. Prior methods face challenges with data needs, generalizatio…
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…
UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation
Yang-Tian Sun, Xin Yu, Zehuan Huang +5
Recently, methods leveraging diffusion model priors to assist monocular geometric estimation (e.g., depth and normal) have gained significant attention due to their strong generali…
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…
MV-Adapter: Multi-view Consistent Image Generation Made Easy
Zehuan Huang, Yuan-Chen Guo, Haoran Wang +4
Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to (1) high computati…
MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation
Zehuan Huang, Yuan-Chen Guo, Xingqiao An +7
This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques…
Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion
Hao Wen, Zehuan Huang, Yaohui Wang +2
Existing single image-to-3D creation methods typically involve a two-stage process, first generating multi-view images, and then using these images for 3D reconstruction. However,…
TELA: Text to Layer-wise 3D Clothed Human Generation
Junting Dong, Qi Fang, Zehuan Huang +4
This paper addresses the task of 3D clothed human generation from textural descriptions. Previous works usually encode the human body and clothes as a holistic model and generate t…
From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation
Zehuan Huang, Hongxing Fan, Lipeng Wang +1
Recent advancements in controllable human image generation have led to zero-shot generation using structural signals (e.g., pose, depth) or facial appearance. Yet, generating human…
EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion
Zehuan Huang, Hao Wen, Junting Dong +8
Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation i…