19 citations · 32 across the 12 of their papers we have counts for
12 papers
RECAP-Forcing: Retaining Content Appearances for Long Video Generation
Haiyang Xu, Zheng Ding, Zhuowen Tu
Long autoregressive video generation faces a fundamental memory challenge: with a finite attention window, a model must decide which information from an ever-expanding history to r…
LC-GRPO: Bridging Train-Inference Gap for Flow-Based GRPO with Langevin Correction
Yingqing Guo, Hui Yuan, Zijian He +2
Flow-based generative models are typically sampled by solving a deterministic ordinary differential equation (ODE), whereas online reinforcement learning requires stochastic rollou…
TreeGRPO: Tree-Advantage GRPO for Online RL Post-Training of Diffusion Models
Zheng Ding, Weirui Ye
Reinforcement learning (RL) post-training is crucial for aligning generative models with human preferences, but its prohibitive computational cost remains a major barrier to widesp…
C3Editor: Achieving Controllable Consistency in 2D Model for 3D Editing
Zeng Tao, Zheng Ding, Zeyuan Chen +3
Existing 2D-lifting-based 3D editing methods often encounter challenges related to inconsistency, stemming from the lack of view-consistent 2D editing models and the difficulty of…
Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos
Weirui Ye, Fangchen Liu, Zheng Ding +3
Simulation offers a promising approach for cheaply scaling training data for generalist policies. To scalably generate data from diverse and realistic tasks, existing algorithms ei…
Explorative Inbetweening of Time and Space
Haiwen Feng, Zheng Ding, Zhihao Xia +4
We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame. Our o…
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
Mengqi Zhang, Yang Fu, Zheng Ding +3
3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper, we propose HOIDiffusion for generating realistic…
Restoration by Generation with Constrained Priors
Zheng Ding, Xuaner Zhang, Zhuowen Tu +1
The inherent generative power of denoising diffusion models makes them well-suited for image restoration tasks where the objective is to find the optimal high-quality image within…
Patched Denoising Diffusion Models For High-Resolution Image Synthesis
Zheng Ding, Mengqi Zhang, Jiajun Wu +1
We propose an effective denoising diffusion model for generating high-resolution images (e.g., 1024512), trained on small-size image patches (e.g., 6464). We name o…
DiffusionRig: Learning Personalized Priors for Facial Appearance Editing
Zheng Ding, Xuaner Zhang, Zhihao Xia +3
We address the problem of learning person-specific facial priors from a small number (e.g., 20) of portrait photos of the same person. This enables us to edit this specific person'…
Open-Vocabulary Universal Image Segmentation with MaskCLIP
Zheng Ding, Jieke Wang, Zhuowen Tu
In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background se…
Guided Variational Autoencoder for Disentanglement Learning
Zheng Ding, Yifan Xu, Weijian Xu +4
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement lear…