Eyeline-Labs/Go-with-the-Flow

Health v2 · maintenance only

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1093 stars · 50 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of the CVPR 2025 Oral paper 'Go-with-the-Flow', which controls motion in video diffusion models by replacing i.i.d. Gaussian noise with warped noise during fine-tuning and inference. It includes a local GUI for cut-and-drag motion specification and scripts to run image-to-video and text-to-video diffusion with controllable camera and object motion.

Use cases

• generate videos with controlled camera motion
• make a still image move with cut-and-drag animation
• transfer motion patterns from one video to another
• control object motion in video diffusion model output
• run text-to-video generation with a motion prior
• animate a segmented object in an image

When to choose

• you need fine-grained control over motion in video diffusion outputs
• you want to animate a still image by dragging objects or specifying camera paths
• you want to transfer motion from a reference video to new content
• you are reproducing or building on the CVPR 2025 warped-noise paper

When to avoid

• you need a polished production video-generation product rather than research code
• you have no GPU available for diffusion inference
• you need a non-Python or fully headless pipeline without the GUI
• you require a permissive license - the license is custom (NOASSERTION)

Facets

library · maturity active

machine-learning deep-learning video-processing image-processing llm-inference deep-learning computer-vision image-processing artificial-intelligence python video-diffusion motion-control warped-noise generative-ai text-to-video image-to-video cvpr-2025 research-code video gpu linux

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markdown · JSON · MCP: product_card(name="Eyeline-Labs/Go-with-the-Flow")