KlingAIResearch/ReCamMaster

Health v2 · maintenance only

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1855 stars · 98 forks observed · 2026-08-28

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

ReCamMaster is a reference implementation of a camera-controlled generative video rendering model that re-renders a single source video along novel camera trajectories using a diffusion-based video model. It includes training and inference code, a model checkpoint built on Wan2.1, and the MultiCamVideo multi-camera dataset rendered in Unreal Engine 5.

Use cases

• re-render a video with a new camera trajectory
• generate novel camera angles from a single video
• stabilize shaky handheld footage by applying smooth camera paths
• generate multi-view videos for 4D reconstruction
• create cinematic camera moves like pans, zooms, and arcs on existing footage
• train a camera-controllable video generation model

When to choose

• you need to change or synthesize camera motion for an existing video
• you want a research-grade codebase and dataset for camera-controlled video generation
• you need multi-view video data for 4D reconstruction or embodied AI research

When to avoid

• you need production-quality output matching the paper's demos - the open-source version lags the hosted model
• you want a simple end-user app rather than running Python inference on a GPU
• you need real-time video processing

Facets

library · maturity active

video-processing machine-learning deep-learning computer-vision computer-vision artificial-intelligence deep-learning python video-generation camera-control diffusion-model 4d-reconstruction video-stabilization research-code iccv-2025 multicamvideo-dataset video gpu linux

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markdown · JSON · MCP: product_card(name="KlingAIResearch/ReCamMaster")