activity
20242026
most citedInFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior

4 citations · 9 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2026

MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation

Yawen Shao, Jie Xiao, Kai Zhu +6

Reinforcement learning (RL) has shown strong potential for aligning generative models with human intent, but adapting RL to masked generative models (MGMs) remains largely underexp…

cs.CV2026

Code Plans, Diffusion Renders: Open-Ended Generative World Modeling

Zixun Fang, Yawen Shao, Kai Zhu +7

We introduce \textbf{CoDeR}, a new paradigm for world modeling. Unlike existing video world models that implicitly represent world dynamics through visual observations, our system…

cs.CL2026

Back on Track: Aligning Rewards and States for Reasoning in Diffusion Large Language Models

Yawen Shao, Jie Xiao, Kai Zhu +6

Reinforcement learning (RL) holds immense promise for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, progress is fundamentally constraine…

cs.LG2025

Anchoring Values in Temporal and Group Dimensions for Flow Matching Model Alignment

Yawen Shao, Jie Xiao, Kai Zhu +4

Group Relative Policy Optimization (GRPO) has proven highly effective in enhancing the alignment capabilities of Large Language Models (LLMs). However, current adaptations of GRPO…

cs.CV2025

WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens

Jian Yang, Dacheng Yin, Xiaoxuan He +6

Recent progress in multimodal large language models (MLLMs) has highlighted the challenge of efficiently bridging pre-trained Vision-Language Models (VLMs) with Diffusion Models. W…

cs.CV2025

Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model

Pingyu Wu, Kai Zhu, Yu Liu +6

Autoregressive image generation aims to predict the next token based on previous ones. However, this process is challenged by the bidirectional dependencies inherent in conventiona…

cs.CV2025

VanGogh: A Unified Multimodal Diffusion-based Framework for Video Colorization

Zixun Fang, Zhiheng Liu, Kai Zhu +5

Video colorization aims to transform grayscale videos into vivid color representations while maintaining temporal consistency and structural integrity. Existing video colorization…

cs.CV2024★ 2 cited

Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning

Fan Lu, Wei Wu, Kecheng Zheng +7

Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have dev…

cs.CV2024★ 1 cited

Improved Video VAE for Latent Video Diffusion Model

Pingyu Wu, Kai Zhu, Yu Liu +4

Variational Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora and other latent video diffusion generation…

cs.CV2024★ 2 cited

ViViD: Video Virtual Try-on using Diffusion Models

Zixun Fang, Wei Zhai, Aimin Su +7

Video virtual try-on aims to transfer a clothing item onto the video of a target person. Directly applying the technique of image-based try-on to the video domain in a frame-wise m…

cs.CV2024★ 4 cited

InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior

Zhiheng Liu, Hao Ouyang, Qiuyu Wang +7

3D Gaussians have recently emerged as an efficient representation for novel view synthesis. This work studies its editability with a particular focus on the inpainting task, which…