This canonical paper page includes Commercialization Proof and Related Resources.
ARXIV:2604.02817 · GENERATIVE VIDEO · SUBMITTED 06 APR · 20:14 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02817GENERATIVE VIDEOSUBMITTED 06 APR · 20:14 UTCFRESHNESS UNKNOWNShubo Lin · Xuanyang Zhang · Wei Cheng · Weiming Hu · Gang Yu · Jin Gao · arXiv
A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.
Opportunity summary
Pain A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency. To address this, we propose MMPhysVideo, the first framework to…
Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. Code availability is flagged…
Generative Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Continue into Read for claims, analysis, references, and neighboring papers.
Opportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.
Loading BUILD…
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
No verified related paper changes yet.
SIGNAL CANVAS HISTORY AND DELTAS
No Signal Canvas history deltas yet.
TIMELINE
Save this paper to start tracking momentum - commits, demos, and score changes appear here.
No tracked events yet.
Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.
0/3 checks · 0%
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
0 refs / 0 sources / 0% coverage
unknown
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
unknown
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
unknown
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.
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
0 references, 0 sources, 0% 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
Paper Pack
10.48550/arXiv.2604.02817A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.
Abstract
Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in video generation through joint multimodal modeling. We recast perceptual cues, specifically semantics, geometry, and spatio-temporal trajectory, into a unified pseudo-RGB format, enabling VDMs to directly capture complex physical dynamics. To mitigate cross-modal interference, we propose a Bidirectionally Controlled Teacher architecture, which utilizes parallel branches to fully decouple RGB and perception processing and adopts two zero-initialized control links to gradually learn pixel-wise consistency. For inference efficiency, the teacher's physical prior is distilled into a single-stream student model via representation alignment. Furthermore, we present MMPhysPipe, a scalable data curation and annotation pipeline tailored for constructing physics-rich multimodal datasets. MMPhysPipe employs a vision-language model (VLM) guided by a chain-of-visual-evidence rule to pinpoint physical subjects, enabling expert models to extract multi-granular perceptual information. Without additional inference costs, MMPhysVideo consistently improves physical plausibility and visual quality over advanced models across various benchmarks and achieves state-of-the-art performance compared to existing methods.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 0% 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 7.0
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. Code availability is flagged in th...
PROBLEM
A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in v...
METHOD
Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in video generati...
WHY NOW
Generative Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in video generation through joint multimodal modeling.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. To address this, we propose MMPhysVideo, the first framework to scale physical plausibility in video generation through joint multimodal modeling.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel-only reconstruction. Code availability is flagged in the production record; the public repository link still needs proof alignment.
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 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Preview the source document here, or use the hero PDF action for a new tab.
Reference metadata is not materialized in the public index yet. The source PDF remains the authority; cache refresh is optional.
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
Foundation
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.
Segment
Generative Video
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2604.02817 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
Not indexed yet
Bluesky
Not indexed yet
Commercially relevant
Conflicting
/api/v1/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling/paper-pack/api/v1/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling/build-passport/api/openapi.json/api/mcpsciencetostartup://surfaces/paper-workspacepaper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state{
"contract_version": "paper-r2",
"paper_id": "c4fa5736-62c2-42b0-b4e2-6196481cf2a8",
"arxiv_id": "2604.02817",
"canonical_route": "/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling",
"active_tab": "synced from current hash by the drawer client",
"selected_artifact": "mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling",
"endpoints": {
"paper_pack": "/api/v1/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling/paper-pack",
"build_passport": "/api/v1/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling/build-passport",
"mcp_resource": "sciencetostartup://surfaces/paper-workspace"
}
}Canonical route, proof status, last verified, refs, sources, and coverage.
Page Freshness
Canonical route: /paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling
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 mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling | Route /paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modelingMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.02817"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "MMPhysVideo: Scaling Physical Plausibility in Video Generation via Joint Multimodal Modeling",
"normalized_query": "2604.02817",
"route": "/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling",
"paper_ref": "mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Verdict, compute envelope, blockers, signature state, and receipt links.
Paper proof page receipt window
/buildability/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling
Subject: MMPhysVideo: Scaling Physical Plausibility in Video Generation via Joint Multimodal Modeling
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
Visual citations from the paper document graph.
The application/ld+json payload rendered for agents.
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "WebPage",
"@id": "https://sciencetostartup.com/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling#webpage",
"url": "https://sciencetostartup.com/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling",
"name": "MMPhysVideo: Scaling Physical Plausibility in Video Generation via Joint Multimodal Modeling",
"description": "A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.",
"isPartOf": {
"@id": "https://sciencetostartup.com/#website"
}
},
{
"@type": "ScholarlyArticle",
"@id": "https://sciencetostartup.com/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling#scholarlyArticle",
"headline": "MMPhysVideo: Scaling Physical Plausibility in Video Generation via Joint Multimodal Modeling",
"description": "A framework for generating physically plausible videos by unifying semantic, geometric, and temporal cues into a pseudo-RGB format, improving visual quality and physical consistency.",
"url": "https://sciencetostartup.com/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling",
"sameAs": "https://arxiv.org/abs/2604.02817",
"identifier": {
"@type": "PropertyValue",
"propertyID": "arXiv",
"value": "2604.02817"
},
"isAccessibleForFree": true,
"isPartOf": {
"@id": "https://sciencetostartup.com/#website"
},
"datePublished": "2026-04-03T07:32:24.000Z",
"author": [
{
"@type": "Person",
"name": "Shubo Lin"
},
{
"@type": "Person",
"name": "Xuanyang Zhang"
},
{
"@type": "Person",
"name": "Wei Cheng"
},
{
"@type": "Person",
"name": "Weiming Hu"
},
{
"@type": "Person",
"name": "Gang Yu"
},
{
"@type": "Person",
"name": "Jin Gao"
}
],
"additionalProperty": [
{
"@type": "PropertyValue",
"propertyID": "viabilityScore",
"value": 7
},
{
"@type": "PropertyValue",
"propertyID": "researchDomain",
"value": "Generative Video"
},
{
"@type": "PropertyValue",
"propertyID": "commercialReadiness",
"value": "code"
}
]
},
{
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Home",
"item": "https://sciencetostartup.com"
},
{
"@type": "ListItem",
"position": 2,
"name": "Generative Video",
"item": "https://sciencetostartup.com/topics"
},
{
"@type": "ListItem",
"position": 3,
"name": "MMPhysVideo: Scaling Physical Plausibility in Video Generati",
"item": "https://sciencetostartup.com/paper/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling"
}
]
}
]
}No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling
Paper ref
mmphysvideo-scaling-physical-plausibility-in-video-generation-via-joint-multimodal-modeling
arXiv id
2604.02817
Generated at
2026-04-06T20:14:01.136Z
Evidence freshness
unknown
Last verification
2026-04-06T20:14:01.136Z
Sources
0
References
0
Coverage
0%
Lineage hash
30457305f6e4e44f2658046f28ea41a66d19190d7539dcb6d29cf962c416c171
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.
Verification pending / evidence receipt incomplete
paper_evidence_receipts.references_count
paper_evidence_receipts.coverage