3 citations · 4 across the 9 of their papers we have counts for
12 papers
Training Skills Like Parameters via Self-Supervised Semantic Diffusion
Mo Li, Zixin Yin, Ting Cao +1
While Large Language Models (LLMs) demonstrate remarkable general instruction-following capabilities, they often fall short of human experts in highly specialized, open-ended domai…
Motion4Motion: Motion Transfer Across Subjects at Inference
Ling-Hao Chen, Zixin Yin, Duomin Wang +2
This work explores the motion transfer from one video to another, which is crucial in animation for diverse characters. Previously, video motion transfer has been largely explored…
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Haiwen Diao, Penghao Wu, Hanming Deng +55
Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fra…
ReasonEdit: Towards Reasoning-Enhanced Image Editing Models
Fukun Yin, Shiyu Liu, Yucheng Han +12
Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion deco…
ConsistEdit: Highly Consistent and Precise Training-free Visual Editing
Zixin Yin, Ling-Hao Chen, Lionel Ni +1
Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current app…
UniVerse-1: Unified Audio-Video Generation via Stitching of Experts
Duomin Wang, Wei Zuo, Aojie Li +7
We introduce UniVerse-1, a unified, Veo-3-like model capable of simultaneously generating coordinated audio and video. To enhance training efficiency, we bypass training from scrat…