VIMI: Grounding Video Generation through Multi-modal Instruction
- URL: http://arxiv.org/abs/2407.06304v1
- Date: Mon, 8 Jul 2024 18:12:49 GMT
- Title: VIMI: Grounding Video Generation through Multi-modal Instruction
- Authors: Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chien Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov,
- Abstract summary: Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining.
We construct a large-scale multimodal prompt dataset by employing retrieval methods to pair in-context examples with the given text prompts.
We finetune the model from the first stage on three video generation tasks, incorporating multi-modal instructions.
- Score: 89.90065445082442
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a lack of visual grounding and restricting their versatility and application in multimodal integration. To address this, we construct a large-scale multimodal prompt dataset by employing retrieval methods to pair in-context examples with the given text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within the same model. In the first stage, we propose a multimodal conditional video generation framework for pretraining on these augmented datasets, establishing a foundational model for grounded video generation. Secondly, we finetune the model from the first stage on three video generation tasks, incorporating multi-modal instructions. This process further refines the model's ability to handle diverse inputs and tasks, ensuring seamless integration of multi-modal information. After this two-stage train-ing process, VIMI demonstrates multimodal understanding capabilities, producing contextually rich and personalized videos grounded in the provided inputs, as shown in Figure 1. Compared to previous visual grounded video generation methods, VIMI can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. Lastly, VIMI also achieves state-of-the-art text-to-video generation results on UCF101 benchmark.
Related papers
- Everything is a Video: Unifying Modalities through Next-Frame Prediction [5.720266474212221]
We introduce a novel framework that extends the concept of task reformulation beyond natural language processing (NLP) to multimodal learning.
We propose to reformulate diverse multimodal tasks into a unified next-frame prediction problem, allowing a single model to handle different modalities without modality-specific components.
Our approach is evaluated on a range of tasks, including text-to-text, image-to-text, video-to-video, video-to-text, and audio-to-text.
arXiv Detail & Related papers (2024-11-15T12:59:37Z) - Vivid-ZOO: Multi-View Video Generation with Diffusion Model [76.96449336578286]
New challenges lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution.
We propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text.
arXiv Detail & Related papers (2024-06-12T21:44:04Z) - Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback [38.708690624594794]
Video and text multimodal alignment remains challenging, primarily due to the deficient volume and quality of multimodal instruction-tune data.
We present a novel alignment strategy that employs multimodal AI system to oversee itself called Reinforcement Learning from AI Feedback (RLAIF)
In specific, we propose context-aware reward modeling by providing detailed video descriptions as context during the generation of preference feedback.
arXiv Detail & Related papers (2024-02-06T06:27:40Z) - GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation [100.23111948079037]
GPT4Video is a unified multi-model framework that empowers Large Language Models with the capability of both video understanding and generation.
Specifically, we develop an instruction-following-based approach integrated with the stable diffusion generative model, which has demonstrated to effectively and securely handle video generation scenarios.
arXiv Detail & Related papers (2023-11-25T04:05:59Z) - MuLTI: Efficient Video-and-Language Understanding with Text-Guided
MultiWay-Sampler and Multiple Choice Modeling [7.737755720567113]
This paper proposes MuLTI, a highly accurate and efficient video-and-language understanding model.
We design a Text-Guided MultiWay-Sampler based on adapt-pooling residual mapping and self-attention modules.
We also propose a new pretraining task named Multiple Choice Modeling.
arXiv Detail & Related papers (2023-03-10T05:22:39Z) - mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image
and Video [89.19867891570945]
mPLUG-2 is a new unified paradigm with modularized design for multi-modal pretraining.
It shares common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
It is flexible to select different modules for different understanding and generation tasks across all modalities including text, image, and video.
arXiv Detail & Related papers (2023-02-01T12:40:03Z) - Collaborative Reasoning on Multi-Modal Semantic Graphs for
Video-Grounded Dialogue Generation [53.87485260058957]
We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video.
The primary challenges of this task lie in (1) the difficulty of integrating video data into pre-trained language models (PLMs)
We propose a multi-agent reinforcement learning method to collaboratively perform reasoning on different modalities.
arXiv Detail & Related papers (2022-10-22T14:45:29Z) - UniVL: A Unified Video and Language Pre-Training Model for Multimodal
Understanding and Generation [76.12027504427708]
This paper proposes UniVL: a Unified Video and Language pre-training model for both multimodal understanding and generation.
It comprises four components, including two single-modal encoders, a cross encoder, and a decoder with the Transformer backbone.
We develop two pre-training strategies, stage by stage pre-training (StagedP) and enhanced video representation (EnhancedV) to make the training process of the UniVL more effective.
arXiv Detail & Related papers (2020-02-15T10:03:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.