BEAT: Body-Expression-Audio-Text Dataset

News

• [Ongoing] SMPL-X version data.
• [2023/01] Provide checkpoints (#14, #16), scripts for rendering (#17), preprocessing (#18).
• [2022/12] Provide data (English) in Zip files (#10).
• [2022/10] Project page and rendered videos are available.
• [2022/08] Data in separated files are available.
• [2022/03] CaMN training scripts from anonymous submission.

Features

• 10-Scale Semantic Relevancy: BEAT provides a score and category-label for semantic relevancy between gestures and speech content: no gestures (0), beat gestures (1), low-middle-high quaility deictic gestures (2,3,4), iconic gestures (5,6,7), metaphoric gestures (8,9,10).
• 8-Class Emotional Gestures: For each speaker, data in speech section are recorded with eight emotions: neutral, happiness, anger, sadness, contempt, surprise, fear, and disgust. Data in conversation are labeled as neutral.
• 4-Modality Captured Data: With 16 cameras motion capture system and iphone arkit, BEAT recorded data in four modalities: 75 joints' motion, 52 dimensions blendshape weights, audio and text.
• 76-Hour and 30-Speaker: BEAT (English data) consists of 10 recorded four hours speakers and 20 recorded one hour speakers.
• 4-Language: BEAT contains four types of languages: English (60h), Chinese (12h), Spanish (2h) and Japanese (2h). For the latter three languages, speakers also record English data to provide paired data.
• 2-Scenario: BEAT provides speech (50%) and conversation (50%) recording.

Benchmark

Gesture Generation on BEAT-16h (speaker 2,4,6,8)

Method	Venue	Input Modalities	FID**	SRGR	BeatAlign	ckpt
Seq2Seq	ICRA'19	text	261.3	0.173	0.729	-
Speech2Gestures	CVPR'19	audio	256.7	0.092	0.751	-
Audio2Gestures	ICCV'21	audio	223.8	0.097	0.766	-
MultiContext	SIGGRAPH ASIA'20	audio, text	176.2 (177.2*)	0.195 (0.227)	0.776 (0.751)	link
CaMN	ECCV'22	audio, text, facial	123.7 (122.8)	0.239 (0.240)	0.783 (0.782)	link

*Checkpoints results trained from this repo. are denoted in parentheses. Results in paper are from codes: seq2seq, s2g, a2g, mutli, camn.

**Pretrained 300D AutoEncoder for FID calculation.

Dataset

Introcution

• name: 1_wayne_0_1_8
  • where 1_wayne is speaker id and name,
  • 0 is the recording type: 0 English Speech, 1 English Conversation, 2 Chinese Speech, 3 Chinese Conversation, 4 Spanish Speech, 5 Spanish Conversation, 6 Japanese Speech, 7 Japanese Conversation.
  • 1_8 is the start and end id for the current sequence, where range is 1-118 for speech and 1-12 for conversation.
  • for speech section: 0-64 neutral, 65-72 happiness, 73-80 anger, 81-86 sadness, 87-94 contempt, 95-102 surprise, 103-110 fear, 111-118 disgust.
• format:
  • 120 FPS .bvh for motion, using Z-up, Y-forward in blender, right-hand system.
  • 60 FPS .json for facial blendshape weights.
  • 16K HZ .wav for audio.
  • .TextGrid for text
  • .csv for emotion label, 0-7: neutral, happiness, anger, sadness, contempt, surprise, fear, and disgust.
  • .txt for semantic label, in types, start, end, duration, score, and keywords.
• missing sequences:
  • speaker 9: 0_2_8, speaker 21: 0_1_8.

Train/val/test split

Script is in /dataloaders/preprocessing.ipynb, ratio: 2880:500:500

split_rule_english = {
    # 4h speakers x 10
    "1, 2, 3, 4, 6, 7, 8, 9, 11, 21":{
        # 48+40+100=188mins each
        "train": [
            "0_9_9", "0_10_10", "0_11_11", "0_12_12", "0_13_13", "0_14_14", "0_15_15", "0_16_16", \
            "0_17_17", "0_18_18", "0_19_19", "0_20_20", "0_21_21", "0_22_22", "0_23_23", "0_24_24", \
            "0_25_25", "0_26_26", "0_27_27", "0_28_28", "0_29_29", "0_30_30", "0_31_31", "0_32_32", \
            "0_33_33", "0_34_34", "0_35_35", "0_36_36", "0_37_37", "0_38_38", "0_39_39", "0_40_40", \
            "0_41_41", "0_42_42", "0_43_43", "0_44_44", "0_45_45", "0_46_46", "0_47_47", "0_48_48", \
            "0_49_49", "0_50_50", "0_51_51", "0_52_52", "0_53_53", "0_54_54", "0_55_55", "0_56_56", \
            
            "0_66_66", "0_67_67", "0_68_68", "0_69_69", "0_70_70", "0_71_71",  \
            "0_74_74", "0_75_75", "0_76_76", "0_77_77", "0_78_78", "0_79_79",  \
            "0_82_82", "0_83_83", "0_84_84", "0_85_85",  \
            "0_88_88", "0_89_89", "0_90_90", "0_91_91", "0_92_92", "0_93_93",  \
            "0_96_96", "0_97_97", "0_98_98", "0_99_99", "0_100_100", "0_101_101",  \
            "0_104_104", "0_105_105", "0_106_106", "0_107_107", "0_108_108", "0_109_109",  \
            "0_112_112", "0_113_113", "0_114_114", "0_115_115", "0_116_116", "0_117_117",  \
            
            "1_2_2", "1_3_3", "1_4_4", "1_5_5", "1_6_6", "1_7_7", "1_8_8", "1_9_9", "1_10_10", "1_11_11",
        ],
        # 8+7+10=25mins each
        "val": [
            "0_57_57", "0_58_58", "0_59_59", "0_60_60", "0_61_61", "0_62_62", "0_63_63", "0_64_64", \
            "0_72_72", "0_80_80", "0_86_86", "0_94_94", "0_102_102", "0_110_110", "0_118_118", \
            "1_12_12",
        ],
        # 8+7+10=25mins each
        "test": [
           "0_1_1", "0_2_2", "0_3_3", "0_4_4", "0_5_5", "0_6_6", "0_7_7", "0_8_8", \
           "0_65_65", "0_73_73", "0_81_81", "0_87_87", "0_95_95", "0_103_103", "0_111_111", \
           "1_1_1",
        ],
    },
    
    # 1h speakers x 20
    "5, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30":{
        # 8+7+20=35mins each
        "train": [
            "0_9_9", "0_10_10", "0_11_11", "0_12_12", "0_13_13", "0_14_14", "0_15_15", "0_16_16", \
            "0_66_66", "0_74_74", "0_82_82", "0_88_88", "0_96_96", "0_104_104", "0_112_112", "0_118_118", \
            "1_2_2", "1_3_3", 
            "1_0_0", "1_4_4", # for speaker 29 only
        ],
        # 4+3.5+5 = 12.5mins each
        # 0_65_a and 0_65_b denote the frist and second half of sequence 0_65_65
        "val": [
            "0_5_5", "0_6_6", "0_7_7", "0_8_8",  \
            "0_65_b", "0_73_b", "0_81_b", "0_87_b", "0_95_b", "0_103_b", "0_111_b", \
            "1_1_b",
        ],
        # 4+3.5+5 = 12.5mins each
        "test": [
           "0_1_1", "0_2_2", "0_3_3", "0_4_4", \
           "0_65_a", "0_73_a", "0_81_a", "0_87_a", "0_95_a", "0_103_a", "0_111_a", \
           "1_1_a",
        ],
    },
}

Other scripts and avatars

• Scripts for videos in rendered videos
• Annotation tools for semantic relevancy.
• Avatars in our paper is from HumanGenerator V3, we could share the avatars after confirming your liscense of HGv3 by email.

Reproduction

Train and test CaMN

0. python == 3.7
1. build folders like:

   audio2pose
   ├── codes
   │   └── audio2pose
   ├── datasets
   │   ├── beat_raw_data
   │   ├── beat_annotations
   │   └── beat_cache
   └── outputs
       └── audio2pose
           ├── custom
           └── wandb   
   

2. download the scripts to codes/audio2pose/
3. run pip install -r requirements.txt in the path ./codes/audio2pose/
4. download full dataset to datasets/beat
5. bulid data cache and calculate mean and std by given number of joints, FPS, speakers using /dataloader/preprocessing.ipynb
6. cd ./dataloaders && python build_vocab.py for language model
7. run python train.py -c ./configs/ae_4english_15_141.yaml for pretrained_ae for FID calculation, or download pretrained ckpt to /datasets/beat_cache/cache_name/weights/
8. run python train.py -c ./configs/camn_4english_15_141.yaml for training or or download pretrained ckpt to /datasets/beat_cache/cache_name/weights/.
9. run python test.py -c ./configs/camn_4english_15_141.yaml for inference.
10. load ./outputs/audio2pose/custom/exp_name/epoch_number/xxx.bvh into blender to visualize the test results.

From data in other dataset (e.g. Trinity)

• refer train and test CaMN for bvh cache
• remove modalities, e.g., remove facial expressions.
  • set facial_rep: None and facial_f: 0 in .yaml
  • set dataset: trinity in .yaml

Citation

BEAT is established for the following research project. Please consider cite our work if you use BEAT dataset.

@article{liu2022beat,
  title   = {BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis},
  author  = {Haiyang Liu, Zihao Zhu, Naoya Iwamoto, Yichen Peng, Zhengqing Li, You Zhou, Elif Bozkurt, Bo Zheng},
  journal = {European Conference on Computer Vision},
  year    = {2022}
}