Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
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Canonical route: /signal-canvas/handx-scaling-bimanual-motion-and-interaction-generation
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Canonical ID handx-scaling-bimanual-motion-and-interaction-generation | Route /signal-canvas/handx-scaling-bimanual-motion-and-interaction-generation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/handx-scaling-bimanual-motion-and-interaction-generationMCP example
{
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"mode": "paper",
"paper_ref": "handx-scaling-bimanual-motion-and-interaction-generation",
"query_text": "Summarize HandX: Scaling Bimanual Motion and Interaction Generation"
}
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{
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"mode": "paper",
"query": "HandX: Scaling Bimanual Motion and Interaction Generation",
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"dataset_ref": null
}Claims: 8
References: 100
Proof: Verification pending
Freshness state: computing
Source paper: HandX: Scaling Bimanual Motion and Interaction Generation
PDF: https://arxiv.org/pdf/2603.28766v1
Repository: https://github.com/handx-project/HandX
Source count: 4
Coverage: 83%
Last proof check: 2026-03-31T20:30:18.829Z
Signal Canvas receipt window
/buildability/handx-scaling-bimanual-motion-and-interaction-generation
Subject: HandX: Scaling Bimanual Motion and Interaction Generation
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
Dimensions overall score 7.0
We consolidate and filter existing datasets for quality, and collect a new motion-capture dataset targeting underrepresented bimanual interactions with detailed finger dynamics.
Explicitly stated in the abstract and supported by the dataset scale table (Table 1).
partial
For scalable annotation, we introduce a decoupled strategy that extracts representative motion features, e.g., contact events and finger flexion, and then leverages reasoning from large language models to produce fine-grained, semantically rich descriptions aligned with these features.
Directly stated in the abstract as a core methodological contribution.
partial
We further observe clear scaling trends: larger models trained on larger, higher-quality datasets produce more semantically coherent bimanual motion.
Explicitly stated in the abstract and supported by a figure showing a log-linear relationship between R-precision and FLOPS.
partial
HandX (Ours) 54.2 5.9 fine-grained 485.7
Direct numeric comparison provided in Table 1, showing HandX's 'fine-grained' annotations at 485.7K vs. others with 'coarse' annotations at much lower counts.
partial
We observe that simply concatenating the three types of prompts degrades performance,e.g., the generated motion may assign right-hand movements to the left hand. To address this issue, we encode three types of prompts separately and add a learnable CLS token to each...
Described in the model architecture section, stating that simple concatenation degrades performance and the proposed separate encoding addresses this.
partial
Overall, existing datasets still lack the precision, diversity, and rich inter-hand contact needed for learning fine-grained bimanual motion from text.
Directly stated as a limitation of prior work that motivates the HandX dataset.
partial
Building on the resulting data and annotations, we benchmark diffusion and autoregressive models with versatile conditioning modes.
Stated in the abstract and supported by the model architecture description showing different conditioning inputs.
partial
We propose contact precision (Cprec), contact recall (Crec), and F1 score (CF1) to evaluate hand contact accuracy.
Explicitly described in the evaluation section as newly proposed metrics.
partial
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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Time to first demo
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Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/handx-scaling-bimanual-motion-and-interaction-generation
Paper ref
handx-scaling-bimanual-motion-and-interaction-generation
arXiv id
2603.28766
Generated at
2026-03-31T20:30:18.829Z
Evidence freshness
stale
Last verification
2026-03-31T20:30:18.829Z
Sources
4
References
100
Coverage
83%
Lineage hash
0af2684a247936f90d99f71c15fe38b1a1214bb5ecb0cc1e36adffef50b266c9
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.
100 refs / 4 sources / Verification pending
distribution_readiness_scores
distribution readiness has not been computed yet