Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation

Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/resonance4d-frequency-domain-motion-supervision-for-preset-free-physical-parameter-learning-in-4d-dynamic-physical-scene

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Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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Canonical ID resonance4d-frequency-domain-motion-supervision-for-preset-free-physical-parameter-learning-in-4d-dynamic-physical-scene | Route /signal-canvas/resonance4d-frequency-domain-motion-supervision-for-preset-free-physical-parameter-learning-in-4d-dynamic-physical-scene

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MCP example

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation

PDF: https://arxiv.org/pdf/2604.01994v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.576Z

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Not build-ready: Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation

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Subject: Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation

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Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
reducing peak GPU memory from over 35 GB to around 20 GB
Implicationpartial
Directly stated in abstract with specific numeric values
Verificationpartial
partial
Evidencepartial
reliable motion supervision often relies on online video diffusion or optical-flow pipelines whose computational cost exceeds that of the simulator itself
Implicationpartial
Directly stated in abstract as a constraint of existing methods
Verificationpartial
partial
Evidencepartial
Existing methods further simplify inverse physical modeling by optimizing only partial material parameters, limiting realism in scenes with complex materials and dynamics
Implicationpartial
Directly stated in abstract as a limitation of existing approaches
Verificationpartial
partial
Evidencepartial
we introduce Dual-domain Motion Supervision (DMS), which combines spatial structural consistency for local deformation with frequency-domain spectral consistency for oscillatory and global dynamic patterns
Implicationpartial
Directly stated in abstract as a key component of the method
Verificationpartial
partial
Evidencepartial
substantially reducing training cost and memory overhead while preserving physically meaningful motion cues
Implicationpartial
Directly stated in abstract as a benefit of the proposed method
Verificationpartial
partial
Evidencepartial
enabling high-fidelity physics-driven 4D simulation on a single consumer-grade GPU
Implicationpartial
Directly stated in abstract as a practical outcome of the method
Verificationpartial
partial
Evidencepartial
we further combine zero-shot text-prompted segmentation with simulation-guided initialization to automatically decompose Gaussians into object-part-level regions
Implicationpartial
Directly stated in abstract as a key methodological component
Verificationpartial
partial
Evidencepartial
support joint optimization of full material parameters
Implicationpartial
Directly stated in abstract as a capability of the method
Verificationpartial
partial

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Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation

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