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Exo2EgoPose: Leveraging Exocentric Demonstrations for Vision-Language guided Egocentric 3D Hand Pose Forecasting

Research Multimodal & Generative

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Overall 69
Content 80
Popularity 43

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Merged summary

TL;DR - Exo2EgoPose forecasts egocentric 3D hand poses from visual observations, language instructions, and pose states, using exocentric demonstrations to overcome limited and unstable first-person views. This representation could improve human-to-robot action transfer for manipulation.

  • Treats 3D hand pose as a shared representation bridging human and robot actions.
  • Reconstructs video- and frame-chunk-level exocentric features to capture spatial context and motion dynamics.
  • Progressively integrates these features through attention and adaptive modulation.
  • Reports substantial gains on three pose benchmarks and improved transfer performance on CALVIN.

Sources (1)

Exo2EgoPose: Leveraging Exocentric Demonstrations for Vision-Language guided Egocentric 3D Hand Pose Forecasting

arXiv cs.CV Zhaofeng Shi, Heqian Qiu, Lanxiao Wang, Xiang Li, Hongliang Li 2026-07-17 arXiv:2607.15890
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-18 14:39:26.837750 UTC

TL;DR - Exo2EgoPose forecasts egocentric 3D hand poses from visual observations, language instructions, and pose states, using exocentric demonstrations to overcome limited and unstable first-person views. This representation could improve human-to-robot action transfer for manipulation.

  • Treats 3D hand pose as a shared representation bridging human and robot actions.
  • Reconstructs video- and frame-chunk-level exocentric features to capture spatial context and motion dynamics.
  • Progressively integrates these features through attention and adaptive modulation.
  • Reports substantial gains on three pose benchmarks and improved transfer performance on CALVIN.
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