2 citations · 4 across the 12 of their papers we have counts for
12 papers
VGGT-
Jianyuan Wang, Minghao Chen, Shangzhan Zhang +7
Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also providing geometry-aware features use…
ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing
Xinheng Li, Minghao Chen, Mengqing Wu +2
Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNNs struggle to learn robust priors for dense…
SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations
Yunnan Wang, Kecheng Zheng, Jianyuan Wang +8
The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both semantic and spatio-temporal info…
LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis
Stanislaw Szymanowicz, Minghao Chen, Jianyuan Wang +2
Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive bi…
URDF-Anything+: End-to-End Generation for Simulation-Ready Articulated Assets
Zhuangzhe Wu, Yue Xin, Chengkai Hou +4
Articulated objects are fundamental for robotics, simulation of physics, and interactive virtual environments. However, recovering them from visual observations is inherently chall…
WorldGen: From Text to Traversable and Interactive 3D Worlds
Dilin Wang, Hyunyoung Jung, Tom Monnier +22
We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descr…
TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility
Saman Motamed, Minghao Chen, Luc Van Gool +1
Despite impressive visual fidelity, modern video generative models frequently produce sequences that violate intuitive physical laws, such as objects floating, teleporting, or morp…
AutoPartGen: Autogressive 3D Part Generation and Discovery
Minghao Chen, Jianyuan Wang, Roman Shapovalov +6
We introduce AutoPartGen, a model that generates objects composed of 3D parts in an autoregressive manner. This model can take as input an image of an object, 2D masks of the objec…
VGGT: Visual Geometry Grounded Transformer
Jianyuan Wang, Minghao Chen, Nikita Karaev +3
We present VGGT, a feed-forward neural network that directly infers all key 3D attributes of a scene, including camera parameters, point maps, depth maps, and 3D point tracks, from…
PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models
Minghao Chen, Roman Shapovalov, Iro Laina +4
Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, l…
DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editing
Minghao Chen, Iro Laina, Andrea Vedaldi
We consider the problem of editing 3D objects and scenes based on open-ended language instructions. A common approach to this problem is to use a 2D image generator or editor to gu…
SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds
Minghao Chen, Junyu Xie, Iro Laina +1
We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research on editing 3D objects primarily concentrated on editing individual objects by leveraging off…