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Dataset Preparation

To train MotionCrafter, please download the following datasets from their official project pages.
Note that the paths such as data_normed_1/... refer to preprocessed versions used in our implementation.
You should first download the raw datasets listed below and then run our preprocessing pipeline to obtain the normalized format.

📦 Dataset Download Links

DynamicReplica

Replica synthetic indoor dataset used in dynamic scene reconstruction research.
Project Page: https://github.com/facebookresearch/Replica-Dataset

GTA-SfM

Synthetic outdoor dataset rendered from GTA for SfM, depth, and motion estimation tasks.
Reference: https://github.com/HKUST-Aerial-Robotics/Flow-Motion-Depth

Kubric

Large-scale synthetic multi-task dataset generated using the Kubric engine from Google Research.
Project Page: https://github.com/google-research/kubric

MatrixCity

Ultra-large-scale city-level synthetic dataset for driving and urban scene perception.
Project Page: https://github.com/city-super/MatrixCity

MVS-Synth (GTAV_720)

Multi-view stereo synthetic dataset rendered from GTA-V.
Dataset Page: https://phuang17.github.io/DeepMVS/mvs-synth.html

Spring

High-quality synthetic dataset for optical flow, scene flow, stereo, and multi-task perception.
Benchmark Website: https://spring-benchmark.org/

Point Odyssey

Large synthetic long-term point tracking dataset.
Project Page: https://pointodyssey.com/

Synthia

Synthetic urban driving dataset widely used for segmentation and depth estimation.
Official Page: https://synthia-dataset.net/

TartanAir

AirSim-based large-scale SLAM and multi-modal perception dataset.
Dataset Page: https://tartanair.org/

Virtual KITTI 2

Photorealistic synthetic clone of KITTI for driving perception tasks.
Dataset Page: https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds/

BlinkVision

Video + event-based multimodal synthetic benchmark.
Website: https://www.blinkvision.org/

OmniWorld

Multi-domain, multi-modal synthetic world model dataset.
Project Page: https://yangzhou24.github.io/OmniWorld/

ScanNet++

High-resolution indoor real-world 3D reconstruction dataset.
Project Website: https://scannetpp.mlsg.cit.tum.de/scannetpp/

📝 Notes

  • The datasets above provide raw data. To reproduce the training pipeline, run the preprocessing script to convert them into the data_normed_1/... structure.
  • Several datasets are large (100–500K frames); please ensure sufficient storage.
  • Open-source preprocessing usage and unified script configuration are documented in datasets/preprocess/README.md.