1 citations · 2 across the 20 of their papers we have counts for
25 papers
GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives
Qize Yu, Lianrui Fan, Boyu Chen +23
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop cont…
XPACE: Joint World and Action Modeling from Heterogeneous Experience
Jiacheng Wei, Jerry Bai, Xiaoyu Yue +13
A general-purpose robot needs to draw on diverse experience, choose actions, and anticipate how those actions will change the world. We introduce XPACE, a unified embodied world mo…
AnyWorld: Factorized Egocentric World Models for Cross-Embodiment Generalization
Cheng Chen, Jerry Bai, Jiacheng Wei +11
Collecting contact-rich robot experiences at scale remains a major bottleneck for generalizable manipulation. Beyond data quantity, robot learning also requires diverse experiences…
ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
Wei Xiao, Weiliang Tang, Yuying Ge +4
Human interventions provide crucial corrective signals for post-training Vision-Language-Action (VLA) models. However, enabling seamless humanoid interventions is a formidable syst…
Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
Lu Qiu, Yizhuo Li, Yi Chen +3
World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However…
UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
Boyu Chen, Yi Chen, Lu Qiu +3
Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross-embodiment…
TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs
Jun Zhang, Teng Wang, Yuying Ge +4
This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in…
ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries
Junfu Pu, Teng Wang, Yixiao Ge +3
The proliferation of hour-long videos (e.g., lectures, podcasts, documentaries) has intensified demand for efficient content structuring. However, existing approaches are constrain…
AudioStory: Generating Long-Form Narrative Audio with Large Language Models
Yuxin Guo, Teng Wang, Yuying Ge +4
Recent advances in text-to-audio (TTA) generation excel at synthesizing short audio clips but struggle with long-form narrative audio, which requires temporal coherence and composi…
ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts
Yuying Ge, Yixiao Ge, Chen Li +15
Real-world user-generated short videos, especially those distributed on platforms such as WeChat Channel and TikTok, dominate the mobile internet. However, current large multimodal…
GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning
Yi Chen, Yuying Ge, Rui Wang +4
Recent reinforcement learning approaches, such as outcome-supervised GRPO, have advanced Chain-of-Thought reasoning in large language models (LLMs), yet their adaptation to multimo…
LoRA-Gen: Specializing Large Language Model via Online LoRA Generation
Yicheng Xiao, Lin Song, Rui Yang +4
Recent advances have highlighted the benefits of scaling language models to enhance performance across a wide range of NLP tasks. However, these approaches still face limitations i…
AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video Generation
Lu Qiu, Yizhuo Li, Yuying Ge +3
Recent advances in AI-generated content (AIGC) have significantly accelerated animation production. To produce engaging animations, it is essential to generate coherent multi-shot…
HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation
Yicheng Xiao, Lin Song, Rui Yang +6
With the advancement of language models, unified multimodal understanding and generation have made significant strides, with model architectures evolving from separated components…
Aligning Latent Spaces with Flow Priors
Yizhuo Li, Yuying Ge, Yixiao Ge +2
This paper presents a novel framework for aligning learnable latent spaces to arbitrary target distributions by leveraging flow-based generative models as priors. Our method first…
AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction
Junhao Cheng, Yuying Ge, Yixiao Ge +2
Recent advancements in image and video synthesis have opened up new promise in generative games. One particularly intriguing application is transforming characters from anime films…
TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
Haokun Lin, Teng Wang, Yixiao Ge +6
Pioneering token-based works such as Chameleon and Emu3 have established a foundation for multimodal unification but face challenges of high training computational overhead and lim…
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1
Yi Chen, Yuying Ge, Rui Wang +4
Recent advancements in Chain of Thought (COT) generation have significantly improved the reasoning capabilities of Large Language Models (LLMs), with reinforcement learning (RL) em…
HaploVL: A Single-Transformer Baseline for Multi-Modal Understanding
Rui Yang, Lin Song, Yicheng Xiao +4
Recent advancements in large language models (LLMs) have significantly propelled the development of large multi-modal models (LMMs), highlighting the potential for general and inte…
GenHancer: Imperfect Generative Models are Secretly Strong Vision-Centric Enhancers
Shijie Ma, Yuying Ge, Teng Wang +3
The synergy between generative and discriminative models receives growing attention. While discriminative Contrastive Language-Image Pre-Training (CLIP) excels in high-level semant…
ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models
Xubing Ye, Yukang Gan, Yixiao Ge +2
Large Vision Language Models (LVLMs) have achieved significant success across multi-modal tasks. However, the computational cost of processing long visual tokens can be prohibitive…
VoCo-LLaMA: Towards Vision Compression with Large Language Models
Xubing Ye, Yukang Gan, Xiaoke Huang +2
Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window and high computational co…
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots
Chengyue Wu, Yixiao Ge, Qiushan Guo +5
The remarkable progress of Multi-modal Large Language Models (MLLMs) has attracted significant attention due to their superior performance in visual contexts. However, their capabi…
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
Yiyuan Zhang, Xiaohan Ding, Kaixiong Gong +3
We propose to improve transformers of a specific modality with irrelevant data from other modalities, e.g., improve an ImageNet model with audio or point cloud datasets. We would l…