Skip to content

Latest commit

 

History

History
191 lines (179 loc) · 7.25 KB

training_commands.md

File metadata and controls

191 lines (179 loc) · 7.25 KB
Raw

Using the commands below you can train different models on different datasets.

NOTE: AMASS and 3DPW settings are simillar to each other.

ST_Trans

Human3.6M

python -m api.train model=st_trans \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=50 \
    pred_frames_num=25 \
    model.loss.nT=25 \
    model.pre_post_process=human3.6m \
    model.n_major_joints=22 \
    model.loss.nJ=32\
    epochs=15

AMASS

python -m api.train model=st_trans \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.loss.nT=25 \
    model.pre_post_process=AMASS \
    model.n_major_joints=18 \
    model.loss.nJ=18

3DPW

python -m api.train model=st_trans \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.loss.nT=25 \
    model.pre_post_process=3DPW \
    model.n_major_joints=18 \
    model.loss.nJ=18

PGBIG

Human3.6M

python -m api.train model=pgbig \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.pre_post_process=human3.6m \
    model.in_features=66 \
    model.loss.nJ=22 \
    model.loss.pre_post_process=human3.6m \
    epochs=50

AMASS

python -m api.train model=pgbig \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.pre_post_process=AMASS \
    model.in_features=54 \
    model.loss.nJ=18 \
    model.loss.pre_post_process=AMASS \
    epochs=50

3DPW

python -m api.train model=pgbig \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.pre_post_process=3DPW \
    model.in_features=54 \
    model.loss.nJ=18 \
    model.loss.pre_post_process=3DPW \
    epochs = 50

History-Repeats-Itself

Human3.6M

python -m api.train model=history_repeats_itself \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    model.modality=Human36 \
    model.in_features=66 \
    obs_frames_num=50 \
    pred_frames_num=25

AMASS

python -m api.train model=history_repeats_itself \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    model.modality=AMASS \
    model.in_features=66 \
    obs_frames_num=50 \
    pred_frames_num=25

3DPW

python -m api.train model=history_repeats_itself \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    model.modality=3DPW \
    model.in_features=66 \
    obs_frames_num=50 \
    pred_frames_num=25

STS-GCN

Human3.6M

python -m api.train model=sts_gcn \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.pre_post_process=human3.6m \
    model.n_major_joints=22 \
    model.loss.nJ=32

AMASS

python -m api.train model=sts_gcn \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.pre_post_process=AMASS \
    model.n_major_joints=18 \
    model.loss.nJ=18

3DPW

python -m api.train model=sts_gcn \
    train_dataset=$DATASET_TRAIN_PATH \
    valid_dataset=$DATASET_VALIDATION_PATH \
    obs_frames_num=10 \
    pred_frames_num=25 \
    model.pre_post_process=3DPW \
    model.n_major_joints=18 \
    model.loss.nJ=18

Results

Here you can find their results on the Human3.6M dataset:

Model $80 ms$ $160 ms$ $320 ms$ $400 ms$ $560 ms$ $720 ms$ $880 ms$ $1000 ms$
STS-GCN 17.7 33.9 56.3 67.5 85.1 99.4 109.9 117.0
STS-GCN + pUAL (ours) 13.2 27.1 54.7 66.2 84.5 97.9 109.3 115.7
HRI* 12.7 26.1 51.5 62.6 80.8 95.1 106.8 113.8
HRI* + pUAL (ours) 11.6 25.3 51.2 62.2 80.1 93.7 105.0 112.1
PGBIG 10.3 22.6 46.6 57.5 76.3 90.9 102.7 110.0
PGBIG + pUAL (ours) 9.6 21.7 46.0 57.1 75.9 90.3 102.1 109.5
ST-Trans 13.0 27.0 52.6 63.2 80.3 93.6 104.7 111.6
ST-Trans + pUAL (ours) 10.4 23.4 48.4 59.2 77.0 90.7 101.9 109.3

Similarly on the AMASS dataset:

Model $80 ms$ $160 ms$ $320 ms$ $400 ms$ $560 ms$ $720 ms$ $880 ms$ $1000 ms$
STS-GCN 13.9 27.6 32.0 43.1 51.2 59.2 63.9 68.7
STS-GCN + pUAL 13.0 27.0 31.6 42.4 50.6 59.1 63.5 68.1
HRI 13.5 27.0 31.3 42.0 50.3 58.6 63.1 67.2
HRI + pUAL 12.8 25.2 31.1 41.4 49.8 58.1 62.7 66.5
PGBIG 14.1 28.4 32.7 43.6 51.8 59.9 64.6 67.9
PGBIG + pUAL 13.2 26.5 32.3 40.9 49.5 58.1 64.4 66.9
ST-Trans 13.6 27.3 31.9 42.5 50.4 58.3 64.0 66.6
ST-Trans + pUAL 12.1 24.8 30.8 39.7 47.8 56.5 63.8 66.7

and the 3DPW dataset:

Model $80 ms$ $160 ms$ $320 ms$ $400 ms$ $560 ms$ $720 ms$ $880 ms$ $1000 ms$
STS-GCN 13.5 26.2 31.4 40.3 47.7 55.0 60.0 62.4
STS-GCN + pUAL 12.8 25.9 31.2 40.0 47.3 54.8 59.8 62.2
HRI 15.9 30.5 33.8 45.0 53.5 62.9 67.6 72.5
HRI + pUAL 14.8 29.6 33.2 44.6 53.2 62.4 67.0 72.2
PGBIG 13.1 25.5 37.0 48.8 57.8 66.9 71.6 75.0
PGBIG + pUAL 12.2 23.5 36.0 47.1 55.7 66.4 71.4 74.5
ST-Trans 12.1 24.5 37.0 47.4 57.6 64.6 70.6 73.8
ST-Trans + pUAL 11.1 22.3 35.0 45.7 53.6 63.6 70.0 73.2