GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure

Harvard University  |  Google DeepMind  |  Massachusetts Institute of Technology

Leslie Gu, Junhwa Hur, Charles Herrmann, Fangneng Zhan, Todd Zickler, Deqing Sun, Hanspeter Pfister

Installation

Clone the repository and set up the environment. This implementation relies on UFM for motion prediction and VGGT for geometry estimation.

Tested Environment: Python 3.11, PyTorch 2.x + CUDA 12.8.

# Clone repository
git clone https://github.com/ShixuanGu/GeCo.git
cd GeCo

# Create environment
conda create -n geco python=3.11 -y
conda activate geco

# Install UFM dependencies
cd external/UFM/UniCeption
pip install -e .
cd ..
pip install -e .

# Verify UFM installation (Optional)
# This generates `ufm_output.png` which should match `examples/example_ufm_output.png`
python uniflowmatch/models/ufm.py

# Install VGGT & GeCo requirements
cd ../..
pip install -r requirements.txt

# Install requirements for test time guidance experiment
pip install -r requirements_guidance.txt

Detecting Deformation on a Single Video

Run GeCo on a single video to generate consistency maps (i.e., Motion Map, Structure Map, and Fused Map).

python demo_detection.py \
  --frame_path examples/deform_house \
  --outdir examples/results

GeCo-Eval Benchmark

To reproduce the benchmark results, ensure the data directory follows the structure below.

GeCo-Eval
├── Gen_Veo3.1
│   ├── indoor_prompts
│   │   ├── b1_0
│   │   │   ├── frame_000001.png
│   │   │   ├── frame_000002.png
│   │   │   └── ...
│   │   ├── b1_1
│   │   └── ...
│   ├── object_centric_prompts
│   ├── outdoor_prompts
│   └── stress_test_prompts
├── Gen_SORA2
│   └── ...
├── ...

Run Evaluation

This script calculates the aggregate GeCo score for a specific model across all categories.

python GeCo-Eval_evaluation.py \
  --frames_root path/to/GeCo-Eval/frames \
  --models Gen_Veo3.1,Gen_SORA2

Test Time Guidance Experiment

Run the following command to generate videos with and without guidance:

python demo_guidance.py \
  --only both \
  --model-path THUDM/CogVideoX-5b \
  --loss-fn residual_motion \
  --fixed-frames "12,24,36,48" \
  --prompt "A steady 360° orbit around a detailed globe on a stand in a book-lined study."

Checklist

• Organize pair-wise demo code

Citation

If you find this code useful for your research, please cite our paper:

@article{gu2025geco,
  title={GeCo: A Differentiable Geometric Consistency Metric for Video Generation}, 
  author={Gu, Leslie and Hur, Junhwa and Herrmann, Charles and Zhan, Fangneng and Zickler, Todd and Sun, Deqing and Pfister, Hanspeter},
  journal={arXiv preprint arXiv:2512.22274},
  year={2025}
}