Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
ICCV, 2025
A method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes.
2025
arxivDSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness
ICCV, 2025
A feed-forward approach to increase the likelihood that the 3D generator outputs stable 3D objects directly.
Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images
ICCV, 2025
A modal for completed 3D reconstruction from partially visible inputs.
Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics
ICCV, 2025
An interactive video generative model that can serve as a motion prior for part-level dynamics.
Semantix: An Energy Guided Sampler for Semantic Style Transfer
ICLR, 2025
Semantic Style Transfer that simultaneously guides both style and appearance transfer.
Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image
3DV, 2025
Stanislaw Szymanowicz*, Eldar Insafutdinov*, Chuanxia Zheng*, Dylan Campbell, João Henriques, Christian Rupprecht, Andrea Vedaldi
A fast, super efficient, trainable on a single GPU in one day for scene 3D reconstruction from a single image.
One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing
TMM, 2025
A unified framework to recover better 2D appearance and 2.5D geometry.
2024
Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
arXiv, 2024
A Feed-Forward model that reconstruct 3D structure and apperance with uncalibrated images.
Explicit Correspondence Matching for Generalizable Neural Radiance Fields
arXiv, 2024
Employing explicit correspondence matching as a geometry prior enables NeRF to generalize across scenes.
A General Protocol to Probe Large Vision Models for 3D Physical Understanding
NeurIPS, 2024
Probe large vision models to determine to what extent they 'understand' different physical properties in an image.
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
NeurIPS, 2024
A feed-forward approach for 360-degree scene-level novel view synthesis using only sparse observations.
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
ECCV, 2024(oral)
Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, Jianfei Cai
A feed-forward approach for efficiently predicting 3D Gaussians from sparse multi-view images in a single forward pass.
DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
ECCV, 2024
A physical interaction with objects in vision for part-level dragging.
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition
ECCV, 2024
A self-organized 3D segmentation/decomposition model via neural implicit surface representation.
Free3D: Consistent Novel View Synthesis without 3D Representation
CVPR, 2024
A method to synthesize consistent novel views from a single image on open-set categories without the need of explicit 3D representations.
Amodal Ground Truth and Completion in the Wild
CVPR, 2024
Setting up a Stable Diffusion based network to solve the amodal completion problem for any category and without occluder mask provided.
A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls
CVPR, 2024
A versatile plug-and-play module to fix the scheduler flaws for diffusion models.
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion
ICLR, 2024
An indoor panorama outpainting model using latent diffusion models with view-consistent.
Bridging Global Context Interactions for High-Fidelity Pluralistic Image Completion
T-PAMI, 2024
PICFormer achieves pluralistic image completion with multiple and diverse solutions using a transformer based architecture.
2023
Cocktail🍸: Mixing Multi-Modality Controls for Text-Conditional Image Generation
NeurIPS, 2023
A generalized framework for multi-modality control based on text-to-image generation.
Online clustered codebook
ICCV, 2023
A simple approach to avoid codebook collapse and achive 100% codebook utilisation.
Vector Quantized Wasserstein Auto-Encoder
ICML, 2023
Minimize the codebook-data distortion as the Wasserstein distance.
UniD3: Unified Discrete Diffusion for Simultaneous Vision-Language Generation
ICLR, 2023
Minghui Hu, Chuanxia Zheng, Heliang Zheng, Tat-Jen Cham, Chaoyue Wang, Zuopeng Yang, Dacheng Tao, P.N.Suganthan
A unified discrete diffusion model for simultaneous vision-language generation.
2022
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
NeurIPS, 2022(spotlight)
A spatially conditional normalization is introduced to address the repeated artifacts in vector quantized methods.
Object-Compositional Neural Implicit Surfaces
ECCV, 2022
Automatically decompose a scene into 3D instance, trained using only 2D semantic lables and images.
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
ECCV, 2022
A model that transfers the 2D semantic map into 3D NeRF, and lets users edit 3D model through 2D semantic input.
Bridging global context interactions for high-fidelity image completion
CVPR, 2022
TFill fills in reasonable contents for both foreground object removal and content completion.
2021
Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition
IJCV, 2021
A high-level scene understanding system that simultaneously models the completed shape and appearance for all instances.
AgileGAN: Stylizing Portraits by Inversion-Consistent Transfer Learning
SIGGRAPH, 2021
A GAN inversion model is trained for Stylizing Portraits.
The Spatially-Correlative Loss for Various Image Translation Tasks
CVPR, 2021
A novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired I2I translation.
2019
Pluralistic (Free-Form) Image Completion
CVPR, 2019IJCV, 2021
Given a masked image, the proposed pic model is able to generate multiple and diverse plausible results.
2018
T2Net: Synthetic-to-Realistic Translation for Depth Estimation Tasks
ECCV, 2018
Without using any real depth map, the proposed model evaluates depth maps on real scenes using only synthetic datasets.