Feature Extraction
Transformers
Safetensors
prism
video
representation-learning
view-invariant
cross-view
egocentric
egoexo4d
emnlp2026
custom_code
Instructions to use litcoderr/prism with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use litcoderr/prism with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="litcoderr/prism", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("litcoderr/prism", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Use the paper's terminology in the lead
Browse files
README.md
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[Paper][paper] 路 [Project page][project] 路 [Code][code]
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PRISM is a video encoder that
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This checkpoint is trained on EgoExo4D, using both ego and exo views and captions generated
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by Gemini 3.
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[Paper][paper] 路 [Project page][project] 路 [Code][code]
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PRISM is a video encoder that captures viewpoint-invariant action semantics, matching the
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same action across egocentric and exocentric views. It decomposes each video into
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view-invariant and view-variant latents and recomposes them across videos under language
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supervision, which stays semantically valid beyond the co-occurrences observed in training.
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This checkpoint is trained on EgoExo4D, using both ego and exo views and captions generated
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by Gemini 3.
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