We collect a large-scale 3D meshed co-speech whole-body dataset that contains more than 40M posture instances across about 4.3K aligned speaker audios, dubbed GES-X. To the best of our knowledge, this is the largest whole-body meshed 3D co-speech gesture dataset, whose duration is 15 times of the next largest one.
Some video-pose examples in GES-X dataset. Original video clips are shown on the left, and the corresponding pose sequences are shown on the right.
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Statistics comparison of our GES-X with existing ones. The dotted line “- - -” separates whether the posture in the dataset is built based on the mesh. As for the duration of meshed whole body co-speech gesture datasets, our GES-X is 15 times of the second best one (i.e.BEAT2).
Dataset statistical comparison between our GES-X and existing meshed co-speech gesture datasets (i.e.BEAT2, TalkSHOW). Our GES-X has a much larger word corpus and a more widely uniform distributed gesture motion degree.
In our work, we focus on the task of vivid and diverse co-speech 3D gesture generation from in-the-wild human voices.
Details about our task are shown in the figure below:
Along with GES-X, we proposed CoCoGesture, a novel framework enabling vivid and diverse gesture synthesis from unseen voice of human speech prompts. Our key insight is built upon the custom-designed pretrain-fintune training paradigm.
Moreover, we designed a novel Mixture-of-Gesture-Experts (MoGE) block to adaptively fuse the audio
embedding from the human speech and the gesture features from the pre-trained
gesture experts with a routing mechanism.
Such an effective manner ensures
audio embedding is temporal coordinated with motion features while preserving the vivid and diverse gesture generation.
Comparison with the state-of-the-art counterparts on BEAT2 and TalkSHOW datasets.
↑ means the higher the better.
↓ indicates the lower the better.
“-” denotes that the method cannot be applied to the TalkSHOW dataset due to the lack of text transcripts.
The term “zero-shot” implies that the dataset contains unseen human voices.
Ablation study on model scale and pre-training setting.
‡ denotes without pre-training stage.
Ablation study of MoGE block on BEAT2 dataset.
The format of a metadata JSON file is shown in below:
{'basic': # Each item in metadata list is a dict of a clip extracted from a longer video {'video_id': 'J-LQxBbQPTY', # Video ID in GES-X 'video_path': 'TED_Talk_Videos_key/J-LQxBbQPTY.mp4', # Relative path of the dataset root path 'video_duration': 792.52, # Duration of the video 'video_resolution': [360, 640], 'video_fps': 25.0, 'clip_id': 'J-LQxBbQPTY_0000000', # Clip ID 'clip_path': 'J-LQxBbQPTY_0000000.mp4', # Relative path of the dataset root path 'clip_duration': 0.96, # Duration of the clip itself/em> 'clip_start_end_idx': [0, 24], # Start frame_id and end frame_id 'clip_start_end_time': ['00:00:00.000', '00:00:00.960'] # Start timestamp and end timestamp }, { ... } }