29 citations · 29 across the 8 of their papers we have counts for
11 papers
Getting Space Right: Agentic Rewards for Image Generation
Xiaofeng Tan, Jiaxin Lin, Haobo Yuan +5
Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming accurate rewards and effective onl…
BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion
Wanjiang Weng, Yongliang Wu, Xiaofeng Tan +3
Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existin…
When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy
Xiaofeng Tan, Jun Liu, Bin-Bin Gao +5
RLHF is widely used to align flow-matching text-to-image models with human preferences, but often leads to severe diversity collapse after fine-tuning. In RL, diversity is often as…
MotionRFT: Unified Reinforcement Fine-Tuning for Text-to-Motion Generation
Xiaofeng Tan, Wanjiang Weng, Hongsong Wang +3
Text-to-motion generation has advanced with diffusion- and flow-based generative models, yet supervised pre-training remains insufficient to align models with high-level objectives…
Bilingual Text-to-Motion Generation: A New Benchmark and Baselines
Wanjiang Weng, Xiaofeng Tan, Xiangbo Shu +3
Text-to-motion generation holds significant potential for cross-linguistic applications, yet it is hindered by the lack of bilingual datasets and the poor cross-lingual semantic un…
EasyTune: Efficient Step-Aware Fine-Tuning for Diffusion-Based Motion Generation
Xiaofeng Tan, Wanjiang Weng, Haodong Lei +1
In recent years, motion generative models have undergone significant advancement, yet pose challenges in aligning with downstream objectives. Recent studies have shown that using d…
ConsistentRFT: Reducing Visual Hallucinations in Flow-based Reinforcement Fine-Tuning
Xiaofeng Tan, Jun Liu, Yuanting Fan +7
Reinforcement Fine-Tuning (RFT) on flow-based models is crucial for preference alignment. However, they often introduce visual hallucinations like over-optimized details and semant…
ReAlign: Text-to-Motion Generation via Step-Aware Reward-Guided Alignment
Wanjiang Weng, Xiaofeng Tan, Junbo Wang +3
Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based me…
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
Can Gao, Xiaofeng Tan, Jie Zhou +2
Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a v…
SoPo: Text-to-Motion Generation Using Semi-Online Preference Optimization
Xiaofeng Tan, Hongsong Wang, Xin Geng +1
Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fi…
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection
Xiaofeng Tan, Hongsong Wang, Xin Geng +1
Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. However, these methods struggle with t…