167 citations · 275 across the 22 of their papers we have counts for
23 papers
Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
Weijie Wang, Qihang Cao, Sensen Gao +10
Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting with the physical…
MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE
Ruijie Zhu, Jiahao Lu, Wenbo Hu +4
We present MotionCrafter, a framework that leverages video generators to jointly reconstruct 4D geometry and estimate dense motion from a monocular video. The key idea is a joint r…
Semantix: An Energy Guided Sampler for Semantic Style Transfer
Huiang He, Minghui Hu, Chuanxia Zheng +2
Recent advances in style and appearance transfer are impressive, but most methods isolate global style and local appearance transfer, neglecting semantic correspondence. Additional…
One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing
Yuzhu Ji, Chuanxia Zheng, Tat-Jen Cham
Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in r…
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition
Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham +1
3D decomposition/segmentation still remains a challenge as large-scale 3D annotated data is not readily available. Contemporary approaches typically leverage 2D machine-generated s…
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
Yuedong Chen, Haofei Xu, Chuanxia Zheng +5
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we bu…
One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls
Minghui Hu, Jianbin Zheng, Chuanxia Zheng +3
It is well known that many open-released foundational diffusion models have difficulty in generating images that substantially depart from average brightness, despite such images b…
Online Clustered Codebook
Chuanxia Zheng, Andrea Vedaldi
Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is increasingly used in representation learning. However, optimizing the codevectors in existing V…
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion
Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham
Generating complete 360-degree panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available. Existing GAN-based approaches fa…
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
Minghui Hu, Jianbin Zheng, Daqing Liu +4
Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of…
Explicit Correspondence Matching for Generalizable Neural Radiance Fields
Yuedong Chen, Haofei Xu, Qianyi Wu +3
We present a new generalizable NeRF method that is able to directly generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The key to…
Vector Quantized Wasserstein Auto-Encoder
Tung-Long Vuong, Trung Le, He Zhao +4
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the sem…
Unified Discrete Diffusion for Simultaneous Vision-Language Generation
Minghui Hu, Chuanxia Zheng, Heliang Zheng +5
The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In thi…
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Chuanxia Zheng, Long Tung Vuong, Jianfei Cai +1
Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches with…
Object-Compositional Neural Implicit Surfaces
Qianyi Wu, Xian Liu, Yuedong Chen +4
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on…
High-Quality Pluralistic Image Completion via Code Shared VQGAN
Chuanxia Zheng, Guoxian Song, Tat-Jen Cham +3
PICNet pioneered the generation of multiple and diverse results for image completion task, but it required a careful balance between loss (diversity) and reconstruct…
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
Yuedong Chen, Qianyi Wu, Chuanxia Zheng +2
Image translation and manipulation have gain increasing attention along with the rapid development of deep generative models. Although existing approaches have brought impressive r…
Synthesizing Photorealistic Images with Deep Generative Learning
Chuanxia Zheng
The goal of this thesis is to present my research contributions towards solving various visual synthesis and generation tasks, comprising image translation, image completion, and c…
Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition
Chuanxia Zheng, Duy-Son Dao, Guoxian Song +2
Existing scene understanding systems mainly focus on recognizing the visible parts of a scene, ignoring the intact appearance of physical objects in the real-world. Concurrently, i…
The Spatially-Correlative Loss for Various Image Translation Tasks
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
We propose a novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency while supporting large appearance changes durin…
Bridging Global Context Interactions for High-Fidelity Image Completion
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai +1
Bridging global context interactions correctly is important for high-fidelity image completion with large masks. Previous methods attempting this via deep or large receptive field…
Pluralistic Image Completion
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for \textbf{…
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
Current methods for single-image depth estimation use training datasets with real image-depth pairs or stereo pairs, which are not easy to acquire. We propose a framework, trained…