Animating General Image with Large Visual Motion Model
Dengsheng Chen, Xiaoming Wei, Xiaolin Wei
2024年份
1顶会引用
摘要
We present the pioneering Large Visual Motion Model (LVMM), meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model, fortified with a wealth of prior knowledge extracted from billions of image pairs, demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result, it can infuse any generic image with authentic dynamic effects, enhancing its visual allure.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper16
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Directly Denoising Diffusion ModelsDan Zhang, Jingjing Wang, Feng LuoICML 2024 · 被引用 11,724 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
相关 Paper
- Generalizable Implicit Motion Modeling for Video Frame InterpolationZujin Guo, Wei Li, Chen Change LoyNeurIPS 2024 · 被引用 24 次
- LLM-grounded Video Diffusion ModelsLong Lian, Baifeng Shi, Adam Yala, Trevor Darrell 等ICLR 2024 · 被引用 87 次
- iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image GenerationZhoujie Fu, Xianfang Zeng, Jinghong Lan, Xinyao Liao 等CVPR 2026 · 被引用 7 次
- DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular AutomataEhsan Pajouheshgar, Yitao Xu, Tong Zhang, Sabine SüsstrunkCVPR 2023
- Learning Fine-Grained Motion Embedding for Landscape AnimationHongwei Xue, Bei Liu, Huan Yang, Jianlong Fu 等ACM MM 2021 · 被引用 9 次
