4 citations · 4 across the 18 of their papers we have counts for
10 papers · 1 filter
OpenT2M: No-frill Motion Generation with Open-source,Large-scale, High-quality Data
Bin Cao, Sipeng Zheng, Hao Luo +3
Text-to-motion (T2M) generation aims to create realistic human movements from text descriptions, with promising applications in animation and robotics. Despite recent progress, cur…
Robust Motion Generation using Part-level Reliable Data from Videos
Boyuan Li, Sipeng Zheng, Bin Cao +2
Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames can…
Being-M0.5: A Real-Time Controllable Vision-Language-Motion Model
Bin Cao, Sipeng Zheng, Ye Wang +5
Human motion generation has emerged as a critical technology with transformative potential for real-world applications. However, existing vision-language-motion models (VLMMs) face…
Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos
Hao Luo, Yicheng Feng, Wanpeng Zhang +7
We introduce Being-H0, a dexterous Vision-Language-Action model (VLA) trained on large-scale human videos. Existing VLAs struggle with complex manipulation tasks requiring high dex…
Unified Multimodal Understanding via Byte-Pair Visual Encoding
Wanpeng Zhang, Yicheng Feng, Hao Luo +4
Multimodal large language models (MLLMs) have made significant progress in vision-language understanding, yet effectively aligning different modalities remains a fundamental challe…
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning
Jiazheng Liu, Sipeng Zheng, Börje F. Karlsson +1
Multimodal large language models (MLLMs), built on large-scale pre-trained vision towers and language models, have shown great capabilities in multimodal understanding. However, mo…
VideoOrion: Tokenizing Object Dynamics in Videos
Yicheng Feng, Yijiang Li, Wanpeng Zhang +4
We present VideoOrion, a Video Large Language Model (Video-LLM) that explicitly captures the key semantic information in videos - the spatial-temporal dynamics of objects throughou…
Scaling Large Motion Models with Million-Level Human Motions
Ye Wang, Sipeng Zheng, Bin Cao +4
Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted toward developing large motion models. Despite some progress, current effor…
UniCode: Learning a Unified Codebook for Multimodal Large Language Models
Sipeng Zheng, Bohan Zhou, Yicheng Feng +2
In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize vi…
Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
Sipeng Zheng, Jiazheng Liu, Yicheng Feng +1
Recent studies have presented compelling evidence that large language models (LLMs) can equip embodied agents with the self-driven capability to interact with the world, which mark…