activity
20232026
most citedRAIN: Your Language Models Can Align Themselves without Finetuning

7 citations · 8 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors

Yunuo Chen, Jingnan Gao, Bing He +6

Pretrained diffusion models provide powerful generative priors for extreme image compression, but their stochastic, iterative generation processes are not designed to decode compre…

cs.CV2026

APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation

Sirui Zhang, Fangyun Wei, Dong Chen +5

Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions. We introduce…

cs.CV2026

LensErase: A Foundation Model for Erase Anything

Yunuo Chen, Xiuyu Wu, Qinhong Yang +11

We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse ob…

cs.CV2026

VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System

Haodong Li, Tianfei Ren, Xiaoxiao Ma +25

Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be i…

cs.CV2026

Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models

Dong Chen, Fangyun Wei, Ziyu Wan +18

We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters acro…

cs.CV2026

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

Yang Yue, Fangyun Wei, Tianyu He +10

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on d…

cs.CV2025

Spatia: Video Generation with Updatable Spatial Memory

Jinjing Zhao, Fangyun Wei, Zhening Liu +3

Existing video generation models struggle to maintain long-term spatial and temporal consistency due to the dense, high-dimensional nature of video signals. To overcome this limita…

cs.CV2025

From Virtual Games to Real-World Play

Wenqiang Sun, Fangyun Wei, Jinjing Zhao +5

We introduce RealPlay, a neural network-based real-world game engine that enables interactive video generation from user control signals. Unlike prior works focused on game-style v…

cs.CV2024★ 1 cited

Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models

Jierun Chen, Fangyun Wei, Jinjing Zhao +5

Referring expression comprehension (REC) involves localizing a target instance based on a textual description. Recent advancements in REC have been driven by large multimodal model…

cs.CL2023★ 7 cited

RAIN: Your Language Models Can Align Themselves without Finetuning

Yuhui Li, Fangyun Wei, Jinjing Zhao +2

Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research typically gathered human preference data and then aligned the pre-trained m…