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ARXIV:2603.25931 · GENERATIVE VIDEO · SUBMITTED 30 MAR · 21:59 UTC · FRESHNESS STALE
ARXIV:2603.25931GENERATIVE VIDEOSUBMITTED 30 MAR · 21:59 UTCFRESHNESS STALEAbolfazl Meyarian · Amin Karimi Monsefi · Rajiv Ramnath · Ser-Nam Lim · arXiv
A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion.
Opportunity summary
Pain A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion.
Evidence 72 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajectories of differing conditions, but…
Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elementary physics because their reconstruction objectives penalize per-frame deviations without distinguishing physically consistent dynamics from impossible ones. Contrastive flow matching offers a principled…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. When applied to Wan 2.1-1.3B, our method improves the physical commonsense score on VideoPhy by 16.7% and 11.3% compared to the baseline and SFT,…
Generative Video moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Opportunity summary
Score4.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion.
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Paper Pack
10.48550/arXiv.2603.25931A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion.
Abstract
Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elementary physics because their reconstruction objectives penalize per-frame deviations without distinguishing physically consistent dynamics from impossible ones. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajectories of differing conditions, but we identify a fundamental obstacle in the text-conditioned video setting: semantic-physics entanglement. Because natural-language prompts couple scene content with physical behavior, naive negative sampling draws conditions whose velocity fields largely overlap with the positive sample's, causing the contrastive gradient to directly oppose the flow-matching objective. We formalize this gradient conflict, deriving a precise alignment condition that reveals when contrastive learning helps versus harms training. Guided by this analysis, we introduce DiReCT (Disentangled Regularization of Contrastive Trajectories), a lightweight post-training framework that decomposes the contrastive signal into two complementary scales: a macro-contrastive term that draws partition-exclusive negatives from semantically distant regions for interference-free global trajectory separation, and a micro-contrastive term that constructs hard negatives sharing full scene semantics with the positive sample but differing along a single, LLM-perturbed axis of physical behavior; spanning kinematics, forces, materials, interactions, and magnitudes. A velocity-space distributional regularizer helps to prevent catastrophic forgetting of pretrained visual quality. When applied to Wan 2.1-1.3B, our method improves the physical commonsense score on VideoPhy by 16.7% and 11.3% compared to the baseline and SFT, respectively, without increasing training time.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified72 refs; 3 sources; 50% coverage.
What was readable
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 4.0
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. When applied to Wan 2.1-1.3B, our method improves the physical commonsense score on VideoPhy by 16.7% and 11.3% compared to the baseline and SFT, respectively, without increasing training time.
PROBLEM
A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajectories of differing conditions, but we identify a...
METHOD
Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elementary physics because their reconstruction objectives penalize per-frame deviations without distinguishing physically consistent dynamics from impossible ones. Contrastiv...
WHY NOW
Generative Video moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajectories of differing conditions, but we identify a fundamental obstacle in the text-conditioned video setting: semantic-physics entanglement.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elementary physics because their reconstruction objectives penalize per-frame deviations without distinguishing physically consistent dynamics from impossible ones. Contrastive flow matching offers a principled remedy by pushing apart velocity-field trajectories of differing conditions, but we identify a fundamental obstacle in the text-conditioned video setting: semantic-physics entanglement.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. When applied to Wan 2.1-1.3B, our method improves the physical commonsense score on VideoPhy by 16.7% and 11.3% compared to the baseline and SFT, respectively, without increasing training time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative Video moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
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A post-training framework to enforce physical consistency in generated videos by disentangling semantic and physical properties of motion.
Segment
Generative Video
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
Direct
Adjacent
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Page Freshness
Canonical route: /paper/direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generation
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Endpoint list, payload shape, route context, and copyable handoff data.
Agent Handoff
Canonical ID direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generation | Route /paper/direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generationMCP example
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}Verdict, compute envelope, blockers, signature state, and receipt links.
Paper proof page receipt window
/buildability/direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generation
Subject: DiReCT: Disentangled Regularization of Contrastive Trajectories for Physics-Refined Video Generation
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Visual citations from the paper document graph.
The application/ld+json payload rendered for agents.
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No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generation
Paper ref
direct-disentangled-regularization-of-contrastive-trajectories-for-physics-refined-video-generation
arXiv id
2603.25931
Generated at
2026-03-30T21:59:13.517Z
Evidence freshness
stale
Last verification
2026-03-30T21:59:13.517Z
Sources
3
References
72
Coverage
50%
Lineage hash
2eba8a25b8ad4e02d61e1d3d345414681a0247bec286e7290d9e0d23388a9037
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
72 refs / 3 sources / Verification pending
repo_url
proof_status
3/3 checks · 100%
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.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
72 refs / 3 sources / 50% 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
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
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.
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
Evidence
72 references, 3 sources, 50% 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
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
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
Cost passport has no observed_usd value.
Gaps
Next test
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.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path