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HumanSplatHMR refines 3D human pose and avatar generation via joint optimization

Researchers have introduced HumanSplatHMR, a novel framework that jointly optimizes 3D human pose estimation and avatar creation from video. This approach addresses limitations in existing methods by integrating pose refinement with differentiable rendering, enabling more accurate and generalizable avatar synthesis for novel views and poses. The system leverages human mesh estimates from pose estimators and backpropagates losses through a differentiable renderer to refine pose parameters, improving alignment and rendering quality in real-world scenarios. AI

影响 This method could improve the realism and accuracy of digital avatars used in virtual reality and motion capture applications.

排序理由 This is a research paper detailing a new method for human avatar creation.

在 arXiv cs.CV 阅读 →

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HumanSplatHMR refines 3D human pose and avatar generation via joint optimization

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yeheng Zong, Pou-Chun Kung, Yike Pan, Seth Isaacson, Yizhou Chen, Ram Vasudevan, Katherine A. Skinner ·

    HumanSplatHMR: Closing the Loop Between Human Mesh Recovery and Gaussian Splatting Avatar

    arXiv:2605.02784v1 Announce Type: new Abstract: Accurately recovering human pose and appearance from video is an essential component of scene reconstruction, with applications to motion capture, motion prediction, virtual reality, and digital twinning. Despite significant interes…

  2. arXiv cs.CV TIER_1 English(EN) · Katherine A. Skinner ·

    HumanSplatHMR: Closing the Loop Between Human Mesh Recovery and Gaussian Splatting Avatar

    Accurately recovering human pose and appearance from video is an essential component of scene reconstruction, with applications to motion capture, motion prediction, virtual reality, and digital twinning. Despite significant interest in building realistic human avatars from video…