16 citations · 30 across the 12 of their papers we have counts for
14 papers
Show, Don't Tell: Morphing Latent Reasoning into Image Generation
Harold Haodong Chen, Xinxiang Yin, Wen-Jie Shu +6
Text-to-image (T2I) generation has achieved remarkable progress, yet existing methods often lack the ability to dynamically reason and refine during generation--a hallmark of human…
Human detectors are surprisingly powerful reward models
Kumar Ashutosh, XuDong Wang, Xi Yin +4
Video generation models have recently achieved impressive visual fidelity and temporal coherence. Yet, they continue to struggle with complex, non-rigid motions, especially when sy…
Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection
Xingyou Yin, Ceyao Zhang, Min Hu +1
Large Language Models (LLMs) have demonstrated effectiveness as zero-shot time series (TS) forecasters. The key challenge lies in tokenizing TS data into textual representations th…
FreeAskWorld: An Interactive and Closed-Loop Simulator for Human-Centric Embodied AI
Yuhang Peng, Yizhou Pan, Xinning He +6
As embodied intelligence emerges as a core frontier in artificial intelligence research, simulation platforms must evolve beyond low-level physical interactions to capture complex,…
KPLM-STA: Physically-Accurate Shadow Synthesis for Human Relighting via Keypoint-Based Light Modeling
Xinhui Yin, Qifei Li, Yilin Guo +2
Image composition aims to seamlessly integrate a foreground object into a background, where generating realistic and geometrically accurate shadows remains a persistent challenge.…
HGC-Avatar: Hierarchical Gaussian Compression for Streamable Dynamic 3D Avatars
Haocheng Tang, Ruoke Yan, Xinhui Yin +5
Recent advances in 3D Gaussian Splatting (3DGS) have enabled fast, photorealistic rendering of dynamic 3D scenes, showing strong potential in immersive communication. However, in d…
FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent-of-Thoughts Reasoning
Haodong Chen, Haojian Huang, XinXiang Yin +1
Video Question Answering (VideoQA) based on Large Language Models (LLMs) has shown potential in general video understanding but faces significant challenges when applied to the inh…
Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only
Xuanqi Gao, Weipeng Jiang, Juan Zhai +4
The advent of neural machine translation (NMT) has revolutionized cross-lingual communication, yet preserving stylistic nuances remains a significant challenge. While existing appr…
PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models
Shi Qiu, Shaoyang Guo, Zhuo-Yang Song +51
Current benchmarks for evaluating the reasoning capabilities of Large Language Models (LLMs) face significant limitations: task oversimplification, data contamination, and flawed e…
Generating Multi-Image Synthetic Data for Text-to-Image Customization
Nupur Kumari, Xi Yin, Jun-Yan Zhu +2
Customization of text-to-image models enables users to insert new concepts or objects and generate them in unseen settings. Existing methods either rely on comparatively expensive…
MotiF: Making Text Count in Image Animation with Motion Focal Loss
Shijie Wang, Samaneh Azadi, Rohit Girdhar +3
Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description, which is also referred to as text-guided image animation. Most existing m…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
AcademicGPT: Empowering Academic Research
Shufa Wei, Xiaolong Xu, Xianbiao Qi +21
Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Yet, many of these advanced LLMs are tailored for broad, g…
Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
Rohit Girdhar, Mannat Singh, Andrew Brown +7
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video…