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Stream3D enables 3D generation from video streams

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Stream3D, a novel mechanism designed to enhance 3D generation from sequential visual data. This system allows existing view-conditioned 3D generators to process monocular streams without retraining by employing a dynamic evidential memory. This memory selectively caches informative frames, preventing temporal inconsistencies and managing memory footprint efficiently. AI

IMPACT Enables more consistent 3D reconstructions from continuous video feeds, potentially improving applications in robotics and augmented reality.

RANK_REASON The cluster describes a new research paper detailing a novel method for 3D generation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Stream3D enables 3D generation from video streams

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The cluster describes a new research paper detailing a novel method for 3D generation.
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140 days old
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

    View-conditioned 3D generators such as SAM 3D, TRELLIS and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visual observation often arrives as long monocular streams. Naively applying these generators to each streaming frame independently …

  2. arXiv cs.CV TIER_1 English(EN) · Fangneng Zhan ·

    Stream3D: Sequential Multi-View 3D Generation via Evidential Memory

    View-conditioned 3D generators such as SAM 3D, TRELLIS and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visual observation often arrives as long monocular streams. Naively applying these generators to each streaming frame independently …

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