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ARXIV:2603.13082 · 3D MOTION EDITING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.130823D MOTION EDITINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance.
Opportunity summary
Pain InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance. We introduce the task of multi-person 3D motion editing, where a target motion is generated from a source and…
Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less explored due to limited paired data and the complexity of inter-person interactions. We introduce the task…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To support this, we propose InterEdit3D, a new dataset with manual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark.
3D Motion Editing moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance.
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Paper Pack
10.48550/arXiv.2603.13082InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance.
Abstract
Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less explored due to limited paired data and the complexity of inter-person interactions. We introduce the task of multi-person 3D motion editing, where a target motion is generated from a source and a text instruction. To support this, we propose InterEdit3D, a new dataset with manual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark. We present InterEdit, a synchronized classifier-free conditional diffusion model for TMME. It introduces Semantic-Aware Plan Token Alignment with learnable tokens to capture high-level interaction cues and an Interaction-Aware Frequency Token Alignment strategy using DCT and energy pooling to model periodic motion dynamics. Experiments show that InterEdit improves text-to-motion consistency and edit fidelity, achieving state-of-the-art TMME performance. The dataset and code will be released at https://github.com/YNG916/InterEdit.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance. We introduce the task of multi-person 3D motion editing, where a target motion is generated from a source and a text instruction.
METHOD
Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less explored due to limited paired data and the complexity of inter-person interactions. We introduce the task of multi-person 3D motion editing, where a tar...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To support this, we propose InterEdit3D, a new dataset with manual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark.
WHY NOW
3D Motion Editing moved forward this cycle; last verified April 2026. Public score 8.0/10.
We propose InterEdit3D, a new dataset with manual two-person motion change annotations
Explicitly stated in the abstract as a core contribution of the paper
partial
It introduces Semantic-Aware Plan Token Alignment with learnable tokens to capture high-level interaction cues
Directly stated in the abstract as a specific technical component of the method
partial
an Interaction-Aware Frequency Token Alignment strategy using DCT and energy pooling to model periodic motion dynamics
Directly stated in the abstract as a specific technical component of the method
partial
We present InterEdit, a synchronized classifier-free conditional diffusion model for TMME
Directly stated in the abstract describing the model architecture
partial
Experiments show that InterEdit improves text-to-motion consistency and edit fidelity
Directly stated in the abstract as an experimental result, though specific metrics are not provided
partial
achieving state-of-the-art TMME performance
Directly stated in the abstract as an experimental result, though specific metrics are not provided
partial
Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less explored due to limited paired data and the complexity of inter-person interactions
Directly stated in the abstract as motivation for the work, though it describes the field rather than the paper's specific contribution
partial
The dataset and code will be released at https://github.com/YNG916/InterEdit
Explicitly stated in the abstract with a specific URL provided
partial
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Concepts
Methods
Materials
Markets
Competitors
InterEdit enables advanced text-guided multi-human 3D motion editing with a new dataset and state-of-the-art performance.
Segment
3D Motion Editing
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 17% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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People
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Gaps
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Regulatory need unclassified.
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People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.