Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MotionCraft: Physics-Based Zero-Shot Video Generation
Authors: Antonio Montanaro, Luca Savant Aira, Emanuele Aiello, Diego Valsesia, Enrico Magli
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present quantitative and qualitative experimental results where we show that our zero-shot MOTIONCRAFT is capable of synthesising realistic videos with finely controlled temporal evolution governed by fluid-dynamics equations, rigid body physics, and multi-agent interaction models, while zero-shot state-of-art techniques cannot. |
| Researcher Affiliation | Academia | Politecnico di Torino |
| Pseudocode | Yes | Algorithm 1: Pseudocode of MOTIONCRAFT |
| Open Source Code | Yes | Project page: https://mezzelfo.github.io/MotionCraft/. |
| Open Datasets | Yes | We conducted a quantitative experiment using the MSU Video Frame Interpolation Benchmark dataset [12], considering only real videos. |
| Dataset Splits | No | The paper does not explicitly mention using a 'validation set' or 'validation split' for its experiments. |
| Hardware Specification | Yes | All our experiments are done on a single NVIDIA A6000 (48GB); |
| Software Dependencies | No | The paper mentions 'runwayml/stable-diffusion-v1-5' as the model and 'OpenCV', 'Φ-flow', and 'agentpy' libraries, but only the Stable Diffusion model is specified with a version number (v1-5). Other key software components lack specific version numbers. |
| Experiment Setup | Yes | We set τ = 400, the number of inference steps (both for DDIM inversion and for inverse diffusion) is set to 200 and the used model is runwayml/stable-diffusion-v1-5 (license Creative ML Open RAIL-M). |