Core Concepts
GPT4Motion leverages the planning capability of GPT-4 and the physical simulation strength of Blender to generate high-quality videos that maintain motion coherency and entity consistency, outperforming existing text-to-video generation methods.
Abstract
The paper proposes GPT4Motion, a training-free framework that combines the advanced planning capability of Large Language Models (LLMs) like GPT-4 and the robust simulation tool, Blender, to efficiently generate text-to-video (T2V) content.
Key highlights:
- GPT4Motion employs GPT-4 to generate Blender scripts based on user prompts, which then drive Blender's built-in physics engine to create fundamental scene components that encapsulate coherent physical motions across frames.
- These scene components, including edge maps and depth maps, are then used as conditions for the Stable Diffusion model to generate the final video frames.
- This approach ensures that the resulting video not only aligns with the textual prompt but also exhibits consistent physical behaviors across all frames.
- Experiments on three basic physical motion scenarios (rigid object drop and collision, cloth draping and swinging, and liquid flow) demonstrate that GPT4Motion can efficiently generate high-quality videos that maintain motion coherency and entity consistency, outperforming existing T2V methods.
- The paper highlights the potential of leveraging LLMs' strategic planning capability and Blender's advanced simulation tools to tackle the challenges of motion incoherence and entity inconsistency in T2V generation.
Stats
"A basketball free falls in the air."
"A white flag flaps in the wind."
"Water flows into a white mug on a table, top-down view."
Quotes
"GPT4Motion employs GPT-4 to generate Blender scripts based on user prompts, which then drive Blender's built-in physics engine to create fundamental scene components that encapsulate coherent physical motions across frames."
"These scene components, including edge maps and depth maps, are then used as conditions for the Stable Diffusion model to generate the final video frames."
"This approach ensures that the resulting video not only aligns with the textual prompt but also exhibits consistent physical behaviors across all frames."