MVLLaVA: An Intelligent Agent for Unified and Flexible Novel View Synthesis

TL;DR

MVLLaVA is a versatile intelligent agent that combines multi-view diffusion models with a large multimodal model to generate novel views from various input types, guided by language instructions, demonstrating high flexibility and effectiveness.

Contribution

The paper presents MVLLaVA, a unified platform integrating multi-view diffusion models with LLaVA, enabling flexible, instruction-guided novel view synthesis across diverse input modalities.

Findings

Demonstrates robust performance across multiple view synthesis tasks.

Shows high adaptability to different input types and instructions.

Validates effectiveness through comprehensive experiments.

Abstract

This paper introduces MVLLaVA, an intelligent agent designed for novel view synthesis tasks. MVLLaVA integrates multiple multi-view diffusion models with a large multimodal model, LLaVA, enabling it to handle a wide range of tasks efficiently. MVLLaVA represents a versatile and unified platform that adapts to diverse input types, including a single image, a descriptive caption, or a specific change in viewing azimuth, guided by language instructions for viewpoint generation. We carefully craft task-specific instruction templates, which are subsequently used to fine-tune LLaVA. As a result, MVLLaVA acquires the capability to generate novel view images based on user instructions, demonstrating its flexibility across diverse tasks. Experiments are conducted to validate the effectiveness of MVLLaVA, demonstrating its robust performance and versatility in tackling diverse novel view synthesis challenges.