Articulated 3D reconstruction has valuable applications in various domains,
yet it remains costly and demands intensive work from domain experts. Recent
advancements in template-free learning methods show promising results with
monocular videos. Nevertheless, these approaches necessitate a comprehensive
coverage of all viewpoints of the subject in the input video, thus limiting
their applicability to casually captured videos from online sources. In this
work, we study articulated 3D shape reconstruction from a single and casually
captured internet video, where the subject's view coverage is incomplete. We
propose DreaMo that jointly performs shape reconstruction while solving the
challenging low-coverage regions with view-conditioned diffusion prior and
several tailored regularizations. In addition, we introduce a skeleton
generation strategy to create human-interpretable skeletons from the learned
neural bones and skinning weights. We conduct our study on a self-collected
internet video collection characterized by incomplete view coverage. DreaMo
shows promising quality in novel-view rendering, detailed articulated shape
reconstruction, and skeleton generation. Extensive qualitative and quantitative
studies validate the efficacy of each proposed component, and show existing
methods are unable to solve correct geometry due to the incomplete view
coverage.

本研究提出了 DreaMo，一种从单个且视角覆盖不完整的网络视频中重建关节 3D 形状的方法，通过使用条件视图扩散先验和一些定制的正则化方法解决了低视角覆盖区域带来的挑战，并引入了骨架生成策略生成可解释的人体骨架。对自行收集的视角覆盖不完整的网络视频进行了广泛的定性和定量研究，结果表明 DreaMo 在新视角渲染、详细关节形状重建和骨架生成方面具有良好的质量，并且现有的方法由于视角覆盖不完整而无法解决正确的几何问题。

DreaMo: 从单个非正式视频进行关节 3D 重建

DreaMo: Articulated 3D Reconstruction From A Single Casual Video

In real-world scenarios, labeled samples for dialogue summarization are
usually limited (i.e., few-shot) due to high annotation costs for high-quality
dialogue summaries. To efficiently learn from few-shot samples, previous works
have utilized massive annotated data from other downstream tasks and then
performed prompt transfer in prompt tuning so as to enable cross-task knowledge
transfer. However, existing general-purpose prompt transfer techniques lack
consideration for dialogue-specific information. In this paper, we focus on
improving the prompt transfer from dialogue state tracking to dialogue
summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which
leverages skeleton generation as extra supervision that functions as a medium
connecting the distinct source and target task and resulting in the model's
better consumption of dialogue state information. To automatically extract
dialogue skeletons as supervised training data for skeleton generation, we
design a novel approach with perturbation-based probes requiring neither
annotation effort nor domain knowledge. Training the model on such skeletons
can also help preserve model capability during prompt transfer. Our method
significantly outperforms existing baselines. In-depth analyses demonstrate the
effectiveness of our method in facilitating cross-task knowledge transfer in
few-shot dialogue summarization.

本研究提出了一种 Skeleton-Assisted Prompt Transfer（SAPT）方法，利用生成骨架作为额外的监督信息来实现对话状态信息的更好使用，以促进少样本对话摘要中的跨任务知识转移。通过基于扰动的探针的新方法自动提取对话框架作为有监督的训练数据，避免了注释工作和领域知识的需求。研究表明，我们的方法显著优于现有的基准，并在促进跨任务知识转移中发挥了作用。