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ARXIV:2603.15975 · MOTION GENERATION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2603.15975MOTION GENERATIONSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks.
Opportunity summary
Pain UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in more diverse…
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. In contrast, our goal is to unlock such priors to support a broad spectrum of downstream motion generation tasks within a single unified framework.
Motion Generation moved forward this cycle; last verified April 2026. Public score 9.0/10.
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UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks.
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10.48550/arXiv.2603.15975UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks.
Abstract
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in more diverse cross-modal and in-context motion generation downstream tasks remains largely unclear. Prior work typically adapts pretrained generative priors to individual downstream tasks in a task-specific manner. In contrast, our goal is to unlock such priors to support a broad spectrum of downstream motion generation tasks within a single unified framework. To bridge this gap, we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations, enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs. Specifically, UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent and employs lightweight temporal fusion to inject in-context cues into the pretrained backbone, with negligible runtime overhead compared to the base model. With this design, 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. Experiments demonstrate that UMO consistently outperforms task-specific and training-free baselines across a wide range of benchmarks, despite using a single unified model. Code and model will be publicly available. Project Page: https://oliver-cong02.github.io/UMO.github.io/
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Commercial
Export
Preparing verified analysis
Dimensions overall score 9.0
PROBLEM
UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in more diverse cross-modal and in-con...
METHOD
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage such single-purp...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. In contrast, our goal is to unlock such priors to support a broad spectrum of downstream motion generation tasks within a single unified framework.
WHY NOW
Motion Generation moved forward this cycle; last verified April 2026. Public score 9.0/10.
we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations
Implication not extracted yet.
partial
enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs
Implication not extracted yet.
partial
UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent
Implication not extracted yet.
partial
employs lightweight temporal fusion to inject in-context cues into the pretrained backbone, with negligible runtime overhead compared to the base model
Implication not extracted yet.
partial
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
Implication not extracted yet.
partial
Experiments demonstrate that UMO consistently outperforms task-specific and training-free baselines across a wide range of benchmarks
Implication not extracted yet.
partial
despite using a single unified model
Implication not extracted yet.
partial
Prior work typically adapts pretrained generative priors to individual downstream tasks in a task-specific manner
Implication not extracted yet.
partial
enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs
Directly stated in abstract as the mechanism for unlocking pretrained model capabilities
partial
UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent
Specifically described in the abstract as a key technical component
partial
with negligible runtime overhead compared to the base model
Explicitly stated in abstract with qualifier 'negligible'
partial
we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations
Explicitly stated in the abstract as the core methodological contribution
partial
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Concepts
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UMO is a unified framework that enhances text-to-motion generation by adapting pretrained models for diverse motion tasks.
Segment
Motion Generation
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Commercial read
9.0/10 public viability
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passport absent
stale
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Technical feasibility
partial
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Evidence
0 references, 0 sources, 33% evidence coverage.
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ARTIFACTS
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