GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning (2311.12631v3)

Abstract: Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually encounter high computational costs and often struggle to produce videos with coherent physical motions. To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of LLMs such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis. Specifically, GPT4Motion employs GPT-4 to generate a Blender script based on a user textual prompt, which commands Blender's built-in physics engine to craft fundamental scene components that encapsulate coherent physical motions across frames. Then these components are inputted into Stable Diffusion to generate a video aligned with the textual prompt. Experimental results on three basic physical motion scenarios, including rigid object drop and collision, cloth draping and swinging, and liquid flow, demonstrate that GPT4Motion can generate high-quality videos efficiently in maintaining motion coherency and entity consistency. GPT4Motion offers new insights in text-to-video research, enhancing its quality and broadening its horizon for further explorations.

• The paper introduces GPT4Motion, a training-free framework that leverages GPT-4 to script Blender simulations for generating coherent physical motion in text-to-video synthesis.
• It integrates Stable Diffusion to convert scripted physics into high-fidelity video, demonstrating improved motion smoothness and temporal continuity across diverse physical scenarios.
• Experimental results show that GPT4Motion outperforms existing models in resource efficiency and physical realism by employing cross-attention in diffusion-based video synthesis.

Scripting Physical Motions in Text-to-Video Generation

The paper investigates the challenging domain of generating coherent physical motions in Text-to-Video (T2V) synthesis, introducing a novel framework termed GPT4Motion. The framework is a training-free enhancement to video synthesis, leveraging the potent combination of LLMs, specifically GPT-4, alongside Blender's physical simulation capabilities and the image generation strength of diffusion models like Stable Diffusion.

Core Approach and Methodology

GPT4Motion innovatively utilizes GPT-4 to script Blender simulations based on textual prompts, addressing core limitations in T2V generation—namely, the preservation of physical motion coherency and entity consistency. By scripting the Blender physics engine to construct scene elements encapsulating fundamental physical motions, the approach allows for robust temporal consistency across generated video frames. These scenes are processed using Stable Diffusion to generate the final video output, bridging the gap between textual input and realistic video representation.

This methodology is explored across three fundamental physical scenarios in motion: rigid object interactions, cloth movements, and liquid dynamics. Each scenario serves to validate GPT4Motion's capability in capturing motion consistency and detail, often a limitation with traditional T2V generation systems. The paper demonstrates quantifiable improvements in maintaining the integrity of motion and continuity, outperforming existing methods in motion smoothness and temporal consistency.

Results and Contributions

GPT4Motion sets itself apart by sidestepping the heavy computational loads associated with training-based approaches, achieving significant resource efficiency without compromising video quality. Experimental results highlight the method's ability to synthesize high-fidelity videos that maintain temporal coherence and align with input text prompts remarkably well.

Quantitatively, the comparison against prominent models such as AnimateDiff, ModelScope, Text2Video-Zero, and DirecT2V shows that GPT4Motion achieves superior performance in generating physically accurate and visually consistent video content. It accomplishes this, particularly, by utilizing cross-attention mechanisms within the diffusion model architecture, allowing for superior temporal coherence relative to baselines.

Theoretical and Practical Implications

On the theoretical front, the paper illustrates the potential for LLMs to significantly aid in complex multimodal generation tasks by effectively bridging textual descriptions and physical simulation environments. This approach broadens the understanding of how pre-trained LLMs like GPT-4 can be leveraged beyond typical language processing tasks to facilitate synthetically rich and contextually accurate multimedia generation.

Practically, GPT4Motion provides a scalable solution to T2V challenges, particularly in scenarios where traditional dataset-dependent training approaches are computationally prohibitive. By incorporating foundational physical rules within the video synthesis framework, this work suggests pathways for further refining the synthesis of contextually and physically coherent video content, making it an important contribution to the field.

In conclusion, this work exemplifies a promising direction for developing efficient T2V systems that can capitalize on the symbiotic capabilities of model-based scripting, physics simulations, and advanced diffusion techniques. Future research directions may involve expanding this framework to encompass even more complex physical interactions and multi-object dynamics, representing the next frontier in high-fidelity video synthesis.