{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/neural_assets","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/11f11c45-6d2a-427f-921f-9a67ff5b46b3","json_url":"https://onlylabs.fyi/signals/11f11c45-6d2a-427f-921f-9a67ff5b46b3/signal.json","generated_at":"2026-06-11T04:00:10.705358+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/11f11c45-6d2a-427f-921f-9a67ff5b46b3","signal_json":"https://onlylabs.fyi/signals/11f11c45-6d2a-427f-921f-9a67ff5b46b3/signal.json","source":"https://github.com/google-deepmind/neural_assets","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Google (DeepMind / Gemini) published google-deepmind/neural_assets (Python). 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Hudson, Igor Gilitschenski, Yusuf Aytar, Sjoerd van Steenkiste, Kelsey Allen, Thomas Kipf_ The code here provides an implementation of our Neural Assets framework in jax, including a conditioning encoder to extract the appearance and pose tokens from input data, and a diffusion-based image generator conditioned on the tokens. We also provide an example notebook showing how to load and process data from a public dataset MOVi, and run the model on it to compute training losses. 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