UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors | Signal Canvas | ScienceToStartup

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UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors

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Proof freshness: stale
Proof status: unverified
Display score: 9/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors

Canonical ID umo-unified-in-context-learning-unlocks-motion-foundation-model-priors | Route /signal-canvas/umo-unified-in-context-learning-unlocks-motion-foundation-model-priors

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source_context

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
employs lightweight temporal fusion to inject in-context cues into the pretrained backbone, with negligible runtime overhead compared to the base model
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
UMO finetunes the pretrained model, originally limited to text-to-motion generation, to support diverse previously unsupported tasks, including temporal inpainting, text-guided motion editing, text-serialized geometric constraints, and multi-identity reaction generation
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Experiments demonstrate that UMO consistently outperforms task-specific and training-free baselines across a wide range of benchmarks
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
despite using a single unified model
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Prior work typically adapts pretrained generative priors to individual downstream tasks in a task-specific manner
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs
Implicationpartial
Directly stated in abstract as the mechanism for unlocking pretrained model capabilities
Verificationpartial
partial
Evidencepartial
UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent
Implicationpartial
Specifically described in the abstract as a key technical component
Verificationpartial
partial
Evidencepartial
with negligible runtime overhead compared to the base model
Implicationpartial
Explicitly stated in abstract with qualifier 'negligible'
Verificationpartial
partial
Evidencepartial
we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations
Implicationpartial
Explicitly stated in the abstract as the core methodological contribution
Verificationpartial
partial