---
title: "ReCamMaster"
description: "ReCamMaster from Kuaishou (Kling Team): camera-controlled video rerendering; Verified."
modelId: "recammaster"
pageSource: generated
---

ReCamMaster re-renders an existing video along a new camera trajectory — camera-controlled generative rendering from a single video. In this catalog the in-tree pipeline, operator, and synthesis runtime live under the Kling namespace, and the runner is verified for the official camera-trajectory video-to-video demo with local ReCamMaster/Wan assets.

<ModelPaperFigures modelId="recammaster" locale="en" />

Project page: [jianhongbai.github.io/ReCamMaster](https://jianhongbai.github.io/ReCamMaster/). Code: [KlingAIResearch/ReCamMaster](https://github.com/KlingAIResearch/ReCamMaster).

<ModelCommandBuilder modelId="recammaster" locale="en" />

## What it is for

Use `recammaster` for camera-conditioned video inference. The recipe binds the recorded camera-control profile.

Per the paper (arXiv 2503.11647), ReCamMaster conditions a pre-trained text-to-video model (Wan-family in the open release) on the source video through frame-dimension conditioning, letting camera trajectories be swapped at generation time. Runtime execution requires local Wan2.1, the ReCamMaster checkpoint, torchvision, and CUDA assets. It is published by Kuaishou (Kling Team).

Catalog tasks include camera-controlled video rerendering. Catalog aliases include `Camera-Controlled Generative Rendering from A Single Video`.

Recorded evaluation entry points include [CameraBench](/docs/evaluation/benchmark-hub/camerabench) (Directly evaluates camera-trajectory control quality).

## Runtime contract

Optional `prompt`, `input_path`, `frames`, `fps`, and `steps`; artifacts `generated_video`, `generated_4d_scene`, and `recammaster.mp4`.

CLI task profiles: `default`. WorldFoundry binds `ReCamMasterPipeline`.

Family status: **Verified**.

Recorded `default` defaults: frames=81, fps=8.

The open-source reference runtime is checkpoint/GPU gated — no empty runner shim is registered.

## Running in WorldFoundry

Launch `worldfoundry-eval run recammaster --pipeline.task-profile default` to follow the camera path. The command builder above switches `--pipeline.task-profile` among recorded tasks and still emits `worldfoundry-eval run`.

It runs in the unified environment `worldfoundry-unified-cu128` (Python 3.11, CUDA 12.8).

Recorded weight repositories include `KlingTeam/ReCamMaster-Wan2.1` and `Wan-AI/Wan2.1-T2V-1.3B`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh recammaster`. Add `--download` to fetch missing public checkpoints.

Status: **Verified**.

<ModelRelatedRecipes modelId="recammaster" locale="en" />