7 papers · 1 filter
Improving Video Generation with Human Feedback
Jie Liu, Gongye Liu, Jiajun Liang +14
Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this w…
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Weiyun Wang, Zhangwei Gao, Lixin Gu +72
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…
Cut2Next: Generating Next Shot via In-Context Tuning
Jingwen He, Hongbo Liu, Jiajun Li +4
Effective multi-shot generation demands purposeful, film-like transitions and strict cinematic continuity. Current methods, however, often prioritize basic visual consistency, negl…
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Haoyi Zhu, Honghui Yang, Xiaoyang Wu +8
In contrast to numerous NLP and 2D vision foundational models, learning a 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data va…
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
Zeren Chen, Ziqin Wang, Zhen Wang +7
Recent studies have demonstrated Large Language Models (LLMs) can extend their zero-shot generalization capabilities to multimodal learning through instruction tuning. As more moda…
VEnhancer: Generative Space-Time Enhancement for Video Generation
Jingwen He, Tianfan Xue, Dongyang Liu +6
We present VEnhancer, a generative space-time enhancement framework that improves the existing text-to-video results by adding more details in spatial domain and synthetic detailed…