AutoVFX: Physically Realistic Video Editing from Natural Language Instructions (2411.02394v1)

Abstract: Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and largely inaccessible to everyday users. In this work, we present AutoVFX, a framework that automatically creates realistic and dynamic VFX videos from a single video and natural language instructions. By carefully integrating neural scene modeling, LLM-based code generation, and physical simulation, AutoVFX is able to provide physically-grounded, photorealistic editing effects that can be controlled directly using natural language instructions. We conduct extensive experiments to validate AutoVFX's efficacy across a diverse spectrum of videos and instructions. Quantitative and qualitative results suggest that AutoVFX outperforms all competing methods by a large margin in generative quality, instruction alignment, editing versatility, and physical plausibility.

• The paper introduces a framework that integrates neural scene modeling, LLM-based code generation, and physical simulation to generate realistic video edits.
• It details a pipeline that reconstructs 3D scenes from video, translates natural language into executable code, and leverages Blender-based VFX modules.
• User studies and experiments demonstrate superior instruction alignment and physical plausibility, making advanced video editing accessible to non-experts.

An Expert Review of "AutoVFX: Physically Realistic Video Editing from Natural Language Instructions"

The paper "AutoVFX: Physically Realistic Video Editing from Natural Language Instructions" presents a novel framework that employs a combination of neural scene modeling, LLM-based code generation, and physical simulation to create dynamic and realistic visual effects (VFX) from single video inputs guided by natural language instructions. The primary goal of AutoVFX is to democratize the VFX creation process, making it accessible to non-experts by simplifying the creation of photorealistic and physically plausible video edits.

Technical Overview

AutoVFX operates through a carefully engineered pipeline which can be distilled into three primary components:

1. 3D Scene Modeling: The framework constructs a holistic scene model integrating geometry, appearance, semantics, and lighting from the input video. Methods like SfM, GSplats, and Neural SDF are employed to build a rich representation of the scene. This model underpins subsequent editing and physical simulations, capturing nuanced details essential for high fidelity rendering.
2. LLM-based Code Generation: This component leverages LLMs to translate human language instructions into executable code. By parsing textually described edits into programs composed of pre-defined functions, AutoVFX establishes an intuitive interface for users, permitting them to seamlessly manipulate visual content.
3. VFX Modules and Physical Simulation: A collection of pre-defined modules allows for intricate scene manipulations—object insertions, texture alterations, animations, and physics-driven interactions—integrated within a Blender framework for rendering. These modules capitalize on Blender's capabilities to simulate and render realistic effects efficiently.

Quantitative and Qualitative Analysis

The framework's efficacy was validated through extensive experiments and comparative studies. AutoVFX outperformed existing methods by notable margins in several critical aspects:

• Generative Quality and Instruction Alignment: By accurately interpreting and executing a wide spectrum of natural language prompts, AutoVFX exhibited superior instruction alignment and editing versatility.
• Physical Plausibility: The system’s ability to integrate physical simulations, akin to traditional VFX processes, ensured realistic outcomes that distinguished it from purely generative models.
• Usability Assessments: User studies highlighted the system's intuitive design and ability to replicate users’ descriptive inputs accurately, suggesting profound implications for how non-expert users can engage with VFX processes.

Implications and Future Work

The paper’s contributions point to broader implications for both the theory and practice of AI-driven video editing. AutoVFX illustrates how complex tasks traditionally requiring expert intervention can be streamlined using AI, offering a paradigm where VFX creation is accessible, interactive, and influenced by open-world instructions. This accessibility may propel innovation across industries reliant on video content, such as advertising, entertainment, and AR/VR applications.

The integration of neural scene modeling with LLM-driven code generation reflects a promising collision of AI subfields, charting pathways for future research. Potential expansions include enhancing the system's capability to handle more complex physical simulations, improving semantic accuracy through refined contextual LLMs, and incorporating more sophisticated materials and particle effects to support a broader range of scenarios.

In conclusion, AutoVFX stands as a compelling example of AI's potential to transform video-editing pipelines through its innovative approach toward automated, instruction-driven VFX creation. Researchers in AI and computer graphics may find this system to be a cornerstone for future developments that blend creativity with computational intelligence.