3 citations · 3 across the 8 of their papers we have counts for
11 papers
Image Diffusion Models Are Good 3D Human Motion Generators
Siyuan Yang, Chong Wang, Hao Cheng +4
Text-to-motion generation typically relies on dataset-specific skeletal representations, with models trained separately for benchmarks with different joint layouts. We investigate…
TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons
Qichen Zheng, Siyuan Yang, Chong Wang +4
Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically bu…
Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion
Chong Wang, Zixuan Fu, Shiqi Huang +3
Pixel-space diffusion Transformers (DiTs) directly operate on high-dimensional visual data, yet their hidden representations typically undergo uniform refinement across depth. Natu…
A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples
Zixuan Fu, Chong Wang, Lanqing Guo +3
Pixel-space diffusion models aim to learn an end-to-end generator directly over raw pixels. This is challenging because a single model must capture both global structure and local…
Improving Flexible Image Tokenizers for Autoregressive Image Generation
Zixuan Fu, Lanqing Guo, Chong Wang +3
Flexible image tokenizers aim to represent an image using an ordered 1D variable-length token sequence. This flexible tokenization is typically achieved through nested dropout, whe…
RF4D:Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes
Jiarui Zhang, Zhihao Li, Chong Wang +1
Neural fields (NFs) have achieved remarkable success in scene reconstruction and novel view synthesis. However, existing NF approaches that rely on RGB or LiDAR inputs often strugg…
Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in Dual
Chong Wang, Lanqing Guo, Zixuan Fu +4
Plug-and-play (PnP) methods offer an iterative strategy for solving image restoration (IR) problems in a zero-shot manner, using a learned \textit{discriminative denoiser} as the i…
Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoisers
Zixuan Fu, Lanqing Guo, Chong Wang +3
Recent advancements in deep learning have shown impressive results in image and video denoising, leveraging extensive pairs of noisy and noise-free data for supervision. However, t…
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
Laniqng Guo, Chong Wang, Yufei Wang +5
Shadow removal aims at restoring the image content within shadow regions, pursuing a uniform distribution of illumination that is consistent between shadow and non-shadow regions.…
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks
Chong Wang, Yi Yu, Lanqing Guo +1
Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. While recent deep learni…
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
Chong Wang, Lanqing Guo, Yufei Wang +3
Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However, conventional DUN aims to recon…