6 papers
MobileWorldBench: Towards Semantic World Modeling For Mobile Agents
Shufan Li, Konstantinos Kallidromitis, Akash Gokul +3
World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world models, these approaches face prac…
UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation
Chaitanya Patel, Hiroki Nakamura, Yuta Kyuragi +3
Egocentric human motion generation and forecasting with scene-context is crucial for enhancing AR/VR experiences, improving human-robot interaction, advancing assistive technologie…
VideoMultiAgents: A Multi-Agent Framework for Video Question Answering
Noriyuki Kugo, Xiang Li, Zixin Li +9
Video Question Answering (VQA) inherently relies on multimodal reasoning, integrating visual, temporal, and linguistic cues to achieve a deeper understanding of video content. Howe…
Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection
Shufan Li, Konstantinos Kallidromitis, Akash Gokul +4
The predominant approach to advancing text-to-image generation has been training-time scaling, where larger models are trained on more data using greater computational resources. W…
OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows
Shufan Li, Konstantinos Kallidromitis, Akash Gokul +4
We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the r…
SegLLM: Multi-round Reasoning Segmentation
XuDong Wang, Shaolun Zhang, Shufan Li +5
We present SegLLM, a novel multi-round interactive reasoning segmentation model that enhances LLM-based segmentation by exploiting conversational memory of both visual and textual…