activity
20232026
most citedQPoser: Quantized Explicit Pose Prior Modeling for Controllable Pose Generation

1 citations · 1 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CV2026

Efficient Scene-Text Refinement via Pixel-Space Grouped Image Generation

Yaoli Liu, Guo-Hua Wang, Yiyang Chen +4

Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and practical use. Scene-text r…

cs.CV2026

3D Geometric Tooth Alignment Planning via Deep Reinforcement Learning

Yong Li, Jianwen Lou, Jiayue Ma +3

3D geometric tooth alignment planning, which determines sequential trajectories from initial malocclusion to the final target alignment, is a cornerstone of modern digital orthodon…

cs.CV2026

Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation

Boyuan Xiao, Bohong Chen, Yumeng Li +3

In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs & VLAs) are powerful tools wi…

cs.LG2026

Can AI Understand the Language of Origami?

Naaisha Agarwal, Yihan Wu, Xin Guan +10

Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason about the generative mechanisms and constr…

cs.CV2025

FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time

Yaoli Liu, Yao-Xiang Ding, Kun Zhou

This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies…

cs.LG2025

Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models

Yifei Peng, Yaoli Liu, Enbo Xia +5

We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves bot…

cs.CV2025

Motion-example-controlled Co-speech Gesture Generation Leveraging Large Language Models

Bohong Chen, Yumeng Li, Youyi Zheng +2

The automatic generation of controllable co-speech gestures has recently gained growing attention. While existing systems typically achieve gesture control through predefined categ…

cs.IR2025

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation

Jingming Liu, Yumeng Li, Wei Shi +3

Recent studies have proposed leveraging Large Language Models (LLMs) as information retrievers through query rewriting. However, for challenging corpora, we argue that enhancing qu…

cs.LG2025

Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning

Yu Jin, Jingming Liu, Zhexu Luo +5

Visual generative abductive learning studies jointly training symbol-grounded neural visual generator and inducing logic rules from data, such that after learning, the visual gener…

cs.CV2024

Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models

Jingming Liu, Yumeng Li, Boyuan Xiao +5

Under pure textual modality, Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning tasks by decomposing them into simpler sub-problems. However, Mu…

cs.CV2024

Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation

Bohong Chen, Yumeng Li, Yao-Xiang Ding +2

Current co-speech motion generation approaches usually focus on upper body gestures following speech contents only, while lacking supporting the elaborate control of synergistic fu…

cs.CV2023★ 1 cited

QPoser: Quantized Explicit Pose Prior Modeling for Controllable Pose Generation

Yumeng Li, Yaoxiang Ding, Zhong Ren +1

Explicit pose prior models compress human poses into latent representations for using in pose-related downstream tasks. A desirable explicit pose prior model should satisfy three d…

cs.AI2023

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings

Yifei Peng, Zijie Zha, Yu Jin +5

Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abducti…