Introducing a revival of PapersWithCode! As @ilyasut said, we're back to the "age of research". Hence, it's important to share research and build on each other's work. > find SOTA per domain, not just LLMs > leaderboards > methods > all parsed at scale using AI agents.

DisCo: Disentangled Control for Referring Human Dance Generation in Real World paper page: huggingface.co/papers/2307.0… Generative AI has made significant strides in computer vision, particularly in image/video synthesis conditioned on text descriptions. Despite the advancements, it remains challenging especially in the generation of human-centric content such as dance synthesis. Existing dance synthesis methods struggle with the gap between synthesized content and real-world dance scenarios. In this paper, we define a new problem setting: Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties: (i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose; (ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses; (iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce a novel approach, DISCO, which includes a novel model architecture with disentangled control to improve the faithfulness and compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions.

Announcing @ICCV_2021 workshop on "Unsupervised 3D Learning in the Wild" with an incredible line-up of speakers on this topic! #ICCV2021 🚩 Website: unsup3d.github.io 📅 Time: 7:00-18:00 EDT / 12:00-23:00 BST, 11 Oct 2021 Calendar: calendar.google.com/calendar… (mark it down!)

Here’s our new computer vision system achieving state of the art results in image segmentation, without needing any labeled training data. This new model was trained on random, unlabeled data, but quickly achieved state-of-the-art results. It’s awesome.