Vivid-ZOO: Multi-View Video Generation with Diffusion Model
- URL: http://arxiv.org/abs/2406.08659v1
- Date: Wed, 12 Jun 2024 21:44:04 GMT
- Title: Vivid-ZOO: Multi-View Video Generation with Diffusion Model
- Authors: Bing Li, Cheng Zheng, Wenxuan Zhu, Jinjie Mai, Biao Zhang, Peter Wonka, Bernard Ghanem,
- Abstract summary: 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.
- Score: 76.96449336578286
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new challenges posed by T2MVid generation lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution. To this end, we propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text. Specifically, we factor the T2MVid problem into viewpoint-space and time components. Such factorization allows us to combine and reuse layers of advanced pre-trained multi-view image and 2D video diffusion models to ensure multi-view consistency as well as temporal coherence for the generated multi-view videos, largely reducing the training cost. We further introduce alignment modules to align the latent spaces of layers from the pre-trained multi-view and the 2D video diffusion models, addressing the reused layers' incompatibility that arises from the domain gap between 2D and multi-view data. In support of this and future research, we further contribute a captioned multi-view video dataset. Experimental results demonstrate that our method generates high-quality multi-view videos, exhibiting vivid motions, temporal coherence, and multi-view consistency, given a variety of text prompts.
Related papers
- MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow [15.155484662231508]
We present MVTokenFlow for high-quality 4D content creation from monocular videos.
We utilize the multiview diffusion model to generate multiview images on different timesteps.
MVTokenFlow further regenerates all the multiview images using the rendered 2D flows as guidance.
arXiv Detail & Related papers (2025-02-17T11:34:58Z) - DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation [54.30327187663316]
DiTCtrl is a training-free multi-prompt video generation method under MM-DiT architectures for the first time.
We analyze MM-DiT's attention mechanism, finding that the 3D full attention behaves similarly to that of the cross/self-attention blocks in the UNet-like diffusion models.
Based on our careful design, the video generated by DiTCtrl achieves smooth transitions and consistent object motion given multiple sequential prompts.
arXiv Detail & Related papers (2024-12-24T18:51:19Z) - 3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement [66.8116563135326]
We present 3DEnhancer, which employs a multi-view latent diffusion model to enhance coarse 3D inputs while preserving multi-view consistency.
Unlike existing video-based approaches, our model supports seamless multi-view enhancement with improved coherence across diverse viewing angles.
arXiv Detail & Related papers (2024-12-24T17:36:34Z) - VIMI: Grounding Video Generation through Multi-modal Instruction [89.90065445082442]
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.
arXiv Detail & Related papers (2024-07-08T18:12:49Z) - Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data [80.92268916571712]
A critical bottleneck is the scarcity of high-quality 3D objects with detailed captions.
We propose Bootstrap3D, a novel framework that automatically generates an arbitrary quantity of multi-view images.
We have generated 1 million high-quality synthetic multi-view images with dense descriptive captions.
arXiv Detail & Related papers (2024-05-31T17:59:56Z) - VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model [34.35449902855767]
Two fundamental questions are what data we use for training and how to ensure multi-view consistency.
We propose a dense consistent multi-view generation model that is fine-tuned from off-the-shelf video generative models.
Our approach can generate 24 dense views and converges much faster in training than state-of-the-art approaches.
arXiv Detail & Related papers (2024-03-18T17:48:15Z) - DrivingDiffusion: Layout-Guided multi-view driving scene video
generation with latent diffusion model [19.288610627281102]
We propose DrivingDiffusion to generate realistic multi-view videos controlled by 3D layout.
Our model can generate large-scale realistic multi-camera driving videos in complex urban scenes.
arXiv Detail & Related papers (2023-10-11T18:00:08Z) - Video Probabilistic Diffusion Models in Projected Latent Space [75.4253202574722]
We propose a novel generative model for videos, coined projected latent video diffusion models (PVDM)
PVDM learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources.
arXiv Detail & Related papers (2023-02-15T14:22:34Z)
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.