36 citations · 45 across the 25 of their papers we have counts for
29 papers
JtT: Rethinking Point Trajectory Diffusion for Diverse and Rigid Motion from an Image
Gabrijel Boduljak, Yushi Lan, Christian Rupprecht +1
We study forecasting rigid-body motion from a single image: generating diverse and physically plausible future trajectories for arbitrary points in a scene, without building rigidi…
PhysiFormer: Learning to Simulate Mechanics in World Space
Yiming Chen, Yushi Lan, Andrea Vedaldi
We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixel space, PhysiFormer represe…
SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration
Jeonghwan Kim, Yushi Lan, Yongwei Chen +3
Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual…
Direct 3D-Aware Object Insertion via Decomposed Visual Proxies
Jingbo Gong, Yikai Wang, Yushi Lan +6
Object insertion aims to seamlessly composite a reference object into a specified region of a background image. Recent diffusion-based methods achieve high visual quality but formu…
ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation
Yu Zhang, Yidi Shao, Wenqi Ouyang +5
Unified and scalable Transformers have recently achieved remarkable success in modeling diverse phenomena traditionally associated with computer graphics, such as 3D visual effects…
Syn4D: A Multiview Synthetic 4D Dataset
Zeren Jiang, Yushi Lan, Yihang Luo +8
Dense 3D reconstruction and tracking of dynamic scenes from monocular video remains an important open challenge in computer vision. Progress in this area has been constrained by th…
PnP-U3D: Plug-and-Play 3D Framework Bridging Autoregression and Diffusion for Unified Understanding and Generation
Yongwei Chen, Tianyi Wei, Yushi Lan +4
The rapid progress of large multimodal models has inspired efforts toward unified frameworks that couple understanding and generation. While such paradigms have shown remarkable su…
4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere
Yihang Luo, Shangchen Zhou, Yushi Lan +2
We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce lim…
Video4Spatial: Towards Visuospatial Intelligence with Context-Guided Video Generation
Zeqi Xiao, Yiwei Zhao, Lingxiao Li +6
We investigate whether video generative models can exhibit visuospatial intelligence, a capability central to human cognition, using only visual data. To this end, we present Video…
ArtiLatent: Realistic Articulated 3D Object Generation via Structured Latents
Honghua Chen, Yushi Lan, Yongwei Chen +1
We propose ArtiLatent, a generative framework that synthesizes human-made 3D objects with fine-grained geometry, accurate articulation, and realistic appearance. Our approach joint…
Towards Geometry-Grounded Dense Semantic Matching with VGGT Priors
Songlin Yang, Tianyi Wei, Yushi Lan +3
Semantic matching aims to establish pixel-level correspondences between instances of the same category and represents a fundamental task in computer vision. Existing approaches suf…
STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
Yushi Lan, Yihang Luo, Fangzhou Hong +7
We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-…
FastMesh: Efficient Artistic Mesh Generation via Component Decoupling
Jeonghwan Kim, Yushi Lan, Armando Fortes +2
Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substan…
WorldMem: Long-term Consistent World Simulation with Memory
Zeqi Xiao, Yushi Lan, Yifan Zhou +4
World simulation has gained increasing popularity due to its ability to model virtual environments and predict the consequences of actions. However, the limited temporal context wi…
Textured 3D Regenerative Morphing with 3D Diffusion Prior
Songlin Yang, Yushi Lan, Honghua Chen +1
Textured 3D morphing creates smooth and plausible interpolation sequences between two 3D objects, focusing on transitions in both shape and texture. This is important for creative…
3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
Yihang Luo, Shangchen Zhou, Yushi Lan +2
Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model gener…
ObjCtrl-2.5D: Training-free Object Control with Camera Poses
Zhouxia Wang, Yushi Lan, Shangchen Zhou +1
This study aims to achieve more precise and versatile object control in image-to-video (I2V) generation. Current methods typically represent the spatial movement of target objects…
SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE
Yongwei Chen, Yushi Lan, Shangchen Zhou +2
Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, mov…
GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation
Yushi Lan, Shangchen Zhou, Zhaoyang Lyu +5
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper intro…
MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors
Honghua Chen, Yushi Lan, Yongwei Chen +2
Drag-based editing has become popular in 2D content creation, driven by the capabilities of image generative models. However, extending this technique to 3D remains a challenge. Ex…
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong +10
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative mo…
LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
Yushi Lan, Fangzhou Hong, Shangchen Zhou +7
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved su…
Learning Dense Correspondence for NeRF-Based Face Reenactment
Songlin Yang, Wei Wang, Yushi Lan +4
Face reenactment is challenging due to the need to establish dense correspondence between various face representations for motion transfer. Recent studies have utilized Neural Radi…
Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing
Yushi Lan, Feitong Tan, Di Qiu +8
We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility. The proposed approach leverage…