7 citations · 17 across the 13 of their papers we have counts for
15 papers
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…
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…
Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models
Yue Han, Chong Li, Zhening Liu +5
Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scal…
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…
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…
VideoVLA: Video Generators Can Be Generalizable Robot Manipulators
Yichao Shen, Fangyun Wei, Zhiying Du +5
Generalization in robot manipulation is essential for deploying robots in open-world environments and advancing toward artificial general intelligence. While recent Vision-Language…
CustomX: Unified Character, Action, and Scene Customization in Video World Models
Yitong Wang, Fangyun Wei, Hongyang Zhang +2
Recent advances in world models have greatly enhanced interactive environment simulation. Existing methods mainly fall into two categories: (1) static world generation models, whic…
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…
EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Yuhui Li, Fangyun Wei, Chao Zhang +1
The sequential nature of modern LLMs makes them expensive and slow, and speculative sampling has proven to be an effective solution to this problem. Methods like EAGLE perform auto…
EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees
Yuhui Li, Fangyun Wei, Chao Zhang +1
Inference with modern Large Language Models (LLMs) is expensive and time-consuming, and speculative sampling has proven to be an effective solution. Most speculative sampling metho…
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…
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
Fangyun Wei, Xi Chen, Lin Luo
Despite their sophisticated capabilities, large language models (LLMs) encounter a major hurdle in effective assessment. This paper first revisits the prevalent evaluation method-m…
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Yu Du, Fangyun Wei, Hongyang Zhang
We introduce AnyTool, a large language model agent designed to revolutionize the utilization of a vast array of tools in addressing user queries. We utilize over 16,000 APIs from R…
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang +1
Autoregressive decoding makes the inference of Large Language Models (LLMs) time-consuming. In this paper, we reconsider speculative sampling and derive two key observations. First…
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…