VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model

Core Concepts

VideoMV introduces a novel framework for consistent multi-view image generation by fine-tuning off-the-shelf video generative models.

Abstract

VideoMV proposes a method for generating dense multi-view images by leveraging video generative models. The approach addresses the challenges of training data scarcity and high time consumption in 3D content creation. By fine-tuning from video generative models, VideoMV achieves improved multi-view consistency compared to existing methods. The use of 3D-Aware Denoising Sampling enhances the quality and efficiency of multi-view image generation. Experimental results demonstrate superior performance in both quantitative metrics and visual effects, outperforming state-of-the-art approaches.

Stats

Our approach can generate 24 dense views with only 4 GPU hours of training.
VideoMV outperforms MVDream in terms of PSNR, SSIM, LPIPS, and flow-warping RMSE.
VideoMV achieves better consistency-related metrics compared to baselines like Zero123 and SyncDreamer.

Quotes

Key Insights Distilled From

VideoMV

by Qi Zuo,Xiaod... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.12010.pdf

Deeper Inquiries

How can the concept of consistent multi-view generation impact other fields beyond virtual reality and entertainment?

Consistent multi-view generation has the potential to revolutionize various industries beyond virtual reality and entertainment. In fields like e-commerce, consistent multi-view image generation can enhance product visualization, allowing customers to view products from different angles before making a purchase decision. This can lead to reduced return rates and increased customer satisfaction. In architecture and real estate, this technology can be used for creating immersive 3D tours of properties, providing clients with a realistic sense of space without physically visiting the location. Additionally, in healthcare, it could aid in medical imaging by generating detailed 3D models for surgical planning or training purposes.

What potential limitations or biases could arise from relying heavily on video generative models for multi-view image generation?

Relying heavily on video generative models for multi-view image generation may introduce certain limitations and biases. One limitation is the risk of overfitting to specific datasets used during training, leading to limited generalizability across diverse datasets or scenarios. Biases may also arise if the training data is not representative enough or contains inherent biases related to demographics, objects represented, or environmental conditions captured in videos. Moreover, there could be challenges in ensuring consistency across all views generated due to variations in lighting conditions, camera angles, or object textures present in the video data.

How might advancements in consistent multi-view generation contribute to the development of AI-generated content in various industries?

Advancements in consistent multi-view generation have significant implications for AI-generated content across industries. In marketing and advertising, AI-generated content with high-quality multi-views can personalize advertisements based on user preferences and behaviors more effectively. For design and manufacturing sectors, AI-generated 3D models from multiple viewpoints can streamline prototyping processes by providing accurate visualizations before physical production begins. In education and training applications, interactive AI-generated content with dynamic views enhances learning experiences through immersive simulations that cater to different learning styles.