ARXIV:2601.20835 · 3D HUMAN-SCENE INTERACTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Open-Vocabulary Functional 3D Human-Scene Interaction Generation

arXiv

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation. The key challenge involves reasoning about both the semantics of functional elements in 3D scenes and the 3D human…

METHOD

Full abstract

Generating 3D humans that functionally interact with 3D scenes remains an open problem with applications in embodied AI, robotics, and interactive content creation. The key challenge involves reasoning about both the semantics of functional elements in 3D scenes and the 3D human poses required to achieve functionality-aware interaction. Unfortunately, existing methods typically lack explicit reasoning over object functionality and the corresponding human-scene contact, resulting in implausible or functionally incorrect interactions. In this work, we propose FunHSI, a training-free, functionality-driven framework that enables functionally correct human-scene interactions from open-vocabulary task prompts. Given a task prompt, FunHSI performs functionality-aware contact reasoning to identify functional scene elements, reconstruct their 3D geometry, and model high-level interactions via a contact graph. We then leverage vision-language models to synthesize a human performing the task in the image and estimate proposed 3D body and hand poses. Finally, the proposed 3D body configuration is refined via stage-wise optimization to ensure physical plausibility and functional correctness. In contrast to existing methods, FunHSI not only synthesizes more plausible general 3D interactions, such as "sitting on a sofa'', while supporting fine-grained functional human-scene interactions, e.g., "increasing the room temperature''. Extensive experiments demonstrate that FunHSI consistently generates functionally correct and physically plausible human-scene interactions across diverse indoor and outdoor scenes.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. The key challenge involves reasoning about both the semantics of functional elements in 3D scenes and the 3D human poses required to achieve functionality-aware…

WHY NOW

3D Human-Scene Interaction moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainGenerate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2601.20835 · 3D HUMAN-SCENE INTERACTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Open-Vocabulary Functional 3D Human-Scene Interaction Generation

arXiv

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

3D Human-Scene Interaction moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainGenerate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2601.20835

Open-Vocabulary Functional 3D Human-Scene Interaction Generation

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Parse run linked

A document parse run is attached to this paper.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon file20 anchorsderived fallback45 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

3D Human-Scene Interaction moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation. The key challenge involves reasoning about both the semantics of functional elements in 3D scenes and the 3D human poses required to achieve functionality-aware interaction.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Generating 3D humans that functionally interact with 3D scenes remains an open problem with applications in embodied AI, robotics, and interactive content creation. The key challenge involves reasoning about both the semantics of functional elements in 3D scenes and the 3D human poses required to achieve functionality-aware interaction.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The key challenge involves reasoning about both the semantics of functional elements in 3D scenes and the 3D human poses required to achieve functionality-aware interaction.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
3D Human-Scene Interaction moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linkedDocument parse run

Markets

3D Human-Scene Interaction

Competitors

not indexed

Competitive landscape

Generate functionally correct 3D human-scene interactions from task prompts for AI, robotics, and content creation.

Segment

3D Human-Scene Interaction

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2601.20835 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(45)

MapAnything: Universal Feed-Forward Metric 3D Reconstruction

2025N. Keetha, Norman Müller et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions

2025Marko Mihajlovic, Siwei Zhang et al.

GenHSI: Controllable Generation of Human-Scene Interaction Videos

2025Zekun Li, Rui Zhou et al.

Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces

2025Chenyangguang Zhang, Alexandros Delitzas et al.

3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer

2025Jiajun Deng, Tianyu He et al.

ZeroHSI: Zero-Shot 4D Human-Scene Interaction by Video Generation

2024Hongjie Li, Hong-Xing Yu et al.

TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions

2024Ilya A. Petrov, R. Marin et al.

LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences

2024Hongyan Zhi, Peihao Chen et al.

PerLA: Perceptive 3D language assistant

2024Guofeng Mei, Wei Lin et al.

CameraHMR: Aligning People with Perspective

2024Priyanka Patel, Michael J. Black

Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning

2024Weitai Kang, Haifeng Huang et al.

WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild

2024Rolandos Alexandros Potamias, Jinglei Zhang et al.

Human-Object Interaction from Human-Level Instructions

2024Zhen Wu, Jiaman Li et al.

SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes

2024Alexandros Delitzas, Ayca Takmaz et al.

Animatable Gaussians: Learning Pose-Dependent Gaussian Maps for High-Fidelity Human Avatar Modeling

2024Zhe Li, Zerong Zheng et al.

Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild

2024Lingni Ma, Yuting Ye et al.

InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction

2024Sirui Xu, Ziyin Wang et al.

Scaling Up Dynamic Human-Scene Interaction Modeling

2024Nan Jiang, Zhiyuan Zhang et al.

GenZI: Zero-Shot 3D Human-Scene Interaction Generation

2023Lei Li, Angela Dai

Showing 20 of 45 references

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

Prior WorkHSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions

7.0

Prior WorkInfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement

7.0

Prior WorkViHOI: Human-Object Interaction Synthesis with Visual Priors

7.0

Prior WorkInteract3D: Compositional 3D Generation of Interactive Objects

7.0

Prior WorkHuman Interaction-Aware 3D Reconstruction from a Single Image

7.0

Extension

Builds On ThisHoi3DGen: Generating High-Quality Human-Object-Interactions in 3D

3.0

Builds On ThisMVHOI: Bridge Multi-view Condition to Complex Human-Object Interaction Video Reenactment via 3D Foundation Model

2.0

Commercially relevant

Higher ViabilityPhyGenHOI: Physically-Aware 4D Generation of Dynamic Human-Object Interactions

8.0

Higher ViabilitySceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation

8.0

Higher ViabilityLearning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations

8.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2601.20835
Route: /paper/open-vocabulary-functional-3d-human-scene-interaction-generation
Active tab: read
Artifact: open-vocabulary-functional-3d-human-scene-interaction-generation

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/open-vocabulary-functional-3d-human-scene-interaction-generation/paper-pack
REST build passport API/api/v1/paper/open-vocabulary-functional-3d-human-scene-interaction-generation/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "7b2ddc00-b701-4506-b960-0777e5a89ee9",
  "arxiv_id": "2601.20835",
  "canonical_route": "/paper/open-vocabulary-functional-3d-human-scene-interaction-generation",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "open-vocabulary-functional-3d-human-scene-interaction-generation",
  "endpoints": {
    "paper_pack": "/api/v1/paper/open-vocabulary-functional-3d-human-scene-interaction-generation/paper-pack",
    "build_passport": "/api/v1/paper/open-vocabulary-functional-3d-human-scene-interaction-generation/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/open-vocabulary-functional-3d-human-scene-interaction-generation

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Open-Vocabulary Functional 3D Human-Scene Interaction Generation

Canonical ID open-vocabulary-functional-3d-human-scene-interaction-generation | Route /paper/open-vocabulary-functional-3d-human-scene-interaction-generation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/open-vocabulary-functional-3d-human-scene-interaction-generation

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2601.20835"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Open-Vocabulary Functional 3D Human-Scene Interaction Generation",
  "normalized_query": "2601.20835",
  "route": "/paper/open-vocabulary-functional-3d-human-scene-interaction-generation",
  "paper_ref": "open-vocabulary-functional-3d-human-scene-interaction-generation",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Open-Vocabulary Functional 3D Human-Scene Interaction Generation

/buildability/open-vocabulary-functional-3d-human-scene-interaction-generation

Watchwatch

Subject: Open-Vocabulary Functional 3D Human-Scene Interaction Generation

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.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/open-vocabulary-functional-3d-human-scene-interaction-generation

Paper ref

open-vocabulary-functional-3d-human-scene-interaction-generation

arXiv id

2601.20835

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

595d61bc637648c72e3d981c24b8848220bf8732938fb8390c82325483968fc6

Canonical opportunity-kernel lineage hash.

Signature state

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.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

Source proof

Visual citation anchors from the paper document graph.

20 anchors

proof blockPage 382%

This equation captures one of the core mathematical components of the system. translation, φ ∈R3 the root orientation, and θ = [θb, θh] the

Page and bbox are available; crop image is pending.

proof blockPage 382%

This equation captures one of the core mathematical components of the system. pose parameters. Here, θb ∈R63 and θh ∈R90 represent

Page and bbox are available; crop image is pending.

proof blockPage 482%

This equation captures one of the core mathematical components of the system. ments. Each edge (b, o) ∈E encodes a contact relation be-

Page and bbox are available; crop image is pending.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Open-Vocabulary Functional 3D Human-Scene Interaction Generation

Open-Vocabulary Functional 3D Human-Scene Interaction Generation

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Claim map

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References(45)

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Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline