BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models

Conceptos Básicos

BIVDiff is a training-free framework that bridges specific image diffusion models and general text-to-video diffusion models to enable flexible and efficient video synthesis for various tasks.

Resumen

The paper proposes a general training-free video synthesis framework called BIVDiff, which bridges specific image diffusion models and general text-to-video diffusion models. The key components are:

1. Frame-wise Video Generation: BIVDiff first uses a task-specific image diffusion model (e.g., ControlNet, Instruct Pix2Pix) to generate the target video in a frame-by-frame manner.

2. Mixed Inversion: To address the distribution shift between image and video diffusion models, BIVDiff introduces Mixed Inversion, which performs DDIM Inversion with both image and video diffusion models and mixes the inverted latents.

3. Video Temporal Smoothing: Finally, BIVDiff inputs the mixed latents into a video diffusion model (e.g., VidRD, ZeroScope) to achieve temporal smoothing and consistency.

This decoupled framework enables flexible model selection for different synthesis purposes, endowing BIVDiff with strong task generalization and high efficiency. Extensive experiments on a wide range of video synthesis tasks, including controllable video generation, video editing, video inpainting, and outpainting, demonstrate the effectiveness and general use of BIVDiff.

Estadísticas

Training video synthesis models requires substantial computing resources.
Even with video foundation models, additional costly training is still required for downstream video synthesis tasks.
Extending image diffusion models to videos in a training-free manner cannot well preserve temporal consistency.
Previous methods are usually designed for one specific task and lack cross-task generality.

Citas

"Make him on the road"
"A white swan moving on the lake"

Ideas clave extraídas de

BIVDiff

by Fengyuan Shi... a las arxiv.org 04-10-2024

https://arxiv.org/pdf/2312.02813.pdf

Consultas más profundas

How can BIVDiff be extended to handle more complex video synthesis tasks, such as video style transfer or video animation

BIVDiff can be extended to handle more complex video synthesis tasks by incorporating advanced techniques and models. For tasks like video style transfer, the framework can integrate style transfer algorithms that can transfer the visual style of one video onto another while maintaining the content. This can be achieved by incorporating style transfer networks or adapting existing image style transfer methods to videos. For video animation, BIVDiff can leverage animation-specific models or techniques to generate dynamic and expressive animations. This may involve incorporating motion prediction models, keyframe generation algorithms, or even integrating with motion capture data for realistic animations.

What are the potential limitations of the Mixed Inversion approach, and how can it be further improved to handle a wider range of video diffusion models

The Mixed Inversion approach in BIVDiff may have limitations in cases where there are significant distribution shifts between the latent spaces of the image and video diffusion models. This can lead to inconsistencies in the generated videos. To address this, the approach can be further improved by implementing adaptive mixing strategies based on the specific characteristics of the models being used. This could involve dynamic adjustment of the mixing ratio based on the complexity of the task or the divergence between the latent distributions. Additionally, incorporating techniques for domain adaptation or latent space alignment could help mitigate distribution mismatches and improve the overall performance of the Mixed Inversion process.

Given the rapid progress in text-to-image and text-to-video generation, how might these technologies impact various creative industries in the future

The advancements in text-to-image and text-to-video generation technologies are poised to have a significant impact on various creative industries in the future. These technologies offer novel ways for artists, designers, and content creators to streamline their workflow, explore new creative possibilities, and enhance the efficiency of content production. In fields like advertising, entertainment, and digital media, text-driven generation models can revolutionize the way visual content is created, allowing for rapid prototyping, personalized content generation, and automated design processes. Moreover, these technologies can democratize content creation by providing accessible tools for individuals with limited artistic skills to generate high-quality visuals and videos. Overall, the integration of text-driven generation models in creative industries is expected to drive innovation, efficiency, and creativity in content creation.