In this paper, we democratise 3D content creation, enabling precise
generation of 3D shapes from abstract sketches while overcoming limitations
tied to drawing skills. We introduce a novel part-level modelling and alignment
framework that facilitates abstraction modelling and cross-modal
correspondence. Leveraging the same part-level decoder, our approach seamlessly
extends to sketch modelling by establishing correspondence between CLIPasso
edgemaps and projected 3D part regions, eliminating the need for a dataset
pairing human sketches and 3D shapes. Additionally, our method introduces a
seamless in-position editing process as a byproduct of cross-modal part-aligned
modelling. Operating in a low-dimensional implicit space, our approach
significantly reduces computational demands and processing time.

本文介绍了一种民主化的三维内容创作方法，能够通过抽象的草图精确生成三维形状，同时克服与绘画技巧相关的限制。我们引入了一种新颖的部分水平建模和对齐框架，促进了抽象建模和跨模式对应关系。利用相同的部分水平解码器，我们的方法能够无缝地扩展到草图建模，通过建立 CLIPasso 边缘地图和投影的三维部分区域之间的对应关系，消除了人类草图和三维形状配对数据集的需求。此外，我们的方法通过交叉模式的部分对齐建模产生了一种无缝的位置调整过程。在低维隐含空间中运作，我们的方法显著降低了计算需求和处理时间。

手绘 3D：从抽象自由手稿到精准 3D 形状

Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes

Despite recent progress in video and language representation learning, the
weak or sparse correspondence between the two modalities remains a bottleneck
in the area. Most video-language models are trained via pair-level loss to
predict whether a pair of video and text is aligned. However, even in paired
video-text segments, only a subset of the frames are semantically relevant to
the corresponding text, with the remainder representing noise; where the ratio
of noisy frames is higher for longer videos. We propose FineCo (Fine-grained
Contrastive Loss for Frame Sampling), an approach to better learn video and
language representations with a fine-grained contrastive objective operating on
video frames. It helps distil a video by selecting the frames that are
semantically equivalent to the text, improving cross-modal correspondence.
Building on the well established VideoCLIP model as a starting point, FineCo
achieves state-of-the-art performance on YouCookII, a text-video retrieval
benchmark with long videos. FineCo also achieves competitive results on
text-video retrieval (MSR-VTT), and video question answering datasets (MSR-VTT
QA and MSR-VTT MC) with shorter videos.

FineCo 使用 Fine-grained Contrastive Loss 对视频帧进行抽样，以更好地学习视频和语言表示，从而提高跨模态对应性和在文本 - 视频检索和视频问答数据集上取得了有竞争力的成果，尤其是在 YouCookII 上实现了最先进的性能。