README.md

metadata

license: apache-2.0

🔥 Reproduce Website Demos

1. [Environment Set Up] Our environment setup is identical to CogVideoX. You can refer to their configuration to complete the environment setup.

   conda create -n robomaster python=3.10
   conda activate robomaster
   

2. Robotic Manipulation on Diverse Out-of-Domain Objects.

   python inference_inthewild.py \
       --input_path demos/diverse_ood_objs \
       --output_path samples/infer_diverse_ood_objs \
       --transformer_path ckpts/RoboMaster \
       --model_path ckpts/CogVideoX-Fun-V1.5-5b-InP
   

3. Robotic Manipulation with Diverse Skills

   python inference_inthewild.py \
       --input_path demos/diverse_skills \
       --output_path samples/infer_diverse_skills \
       --transformer_path ckpts/RoboMaster \
       --model_path ckpts/CogVideoX-Fun-V1.5-5b-InP
   

4. Long Video Generation in Auto-Regressive Manner

   python inference_inthewild.py \
       --input_path demos/long_video \
       --output_path samples/long_video \
       --transformer_path ckpts/RoboMaster \
       --model_path ckpts/CogVideoX-Fun-V1.5-5b-InP
   

🚀 Benchmark Evaluation (Reproduce Paper Results)

├── RoboMaster
  ├── eval_metrics
      ├── VBench
      ├── common_metrics_on_video_quality
      ├── eval_traj
      ├── results
          ├── bridge_eval_gt
          ├── bridge_eval_ours
          ├── bridge_eval_ours_tracking

(1) Inference on Benchmark & Prepare Evaluation Files

1. Generating bridge_eval_ours. (Note that the results may vary slightly across different computing machines, even with the same seed. We have prepared the reference files under eval_metrics/results)

   cd RoboMaster/
   python inference_eval.py
   

2. Generating bridge_eval_ours_tracking: Install CoTracker3, and then estimate tracking points with grid size 30 on bridge_eval_ours.

(2) Evaluation on Visual Quality

1. Evaluation of VBench metrics.

   cd eval_metrics/VBench
   python evaluate.py \
       --dimension aesthetic_quality imaging_quality temporal_flickering motion_smoothness subject_consistency background_consistency \
       --videos_path ../results/bridge_eval_ours \
       --mode=custom_input \
       --output_path evaluation_results
   

2. Evaluation of FVD and FID metrics.

   cd eval_metrics/common_metrics_on_video_quality
   python calculate.py -v1_f ../results/bridge_eval_ours -v2_f ../results/bridge_eval_gt
   python -m pytorch_fid eval_1 eval_2
   

(3) Evaluation on Trajectory (Robotic Arm & Manipulated Object)

1. Estimation of TrajError metrics. (Note that we exclude some samples listed in failed_track.txt, due to failed estimation by CoTracker3)

   cd eval_metrics/eval_traj
   python calculate_traj.py \
       --input_path_1 ../results/bridge_eval_ours \
       --input_path_2 ../results/bridge_eval_gt \
       --tracking_path ../results/bridge_eval_ours_tracking \
       --output_path evaluation_results
   

2. Check the visualization videos under evaluation_results. We blend the trajectories of robotic arm and object throughout the entire video for better illustration.