Analternativeapproach isto predict instead adistributionp(ˆy|x) = Φˆy(x) over possible values of the annotationy. Theannotators are shown a set of points sampled randomly and uniformly over one of predefined body parts of aperson inan image.

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In this work, we focus on the task of learning and representing dense correspondences in deformable object categories. While this problem has been considered before, solutions so far have been rather ad-hoc for specific object types (i.e., humans), often with significant manual work involved. However, scaling the geometry understanding to all objects in nature requires more automated approaches that can also express correspondences between related, but geometrically different objects. To this end, we propose a new, learnable image-based representation of dense correspondences. Our model predicts, for each pixel in a 2D image, an embedding vector of the corresponding vertex in the object mesh, therefore establishing dense correspondences between image pixels and 3D object geometry. We demonstrate that the proposed approach performs on par or better than the state-of-the-art methods for dense pose estimation for humans, while being conceptually simpler. We also collect a new in-the-wild dataset of dense correspondences for animal classes and demonstrate that our framework scales naturally to the new deformable object categories.

We propose a zero-shot approach for consistent Text-to-Animated-Characters synthesis based on pre-trained Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consistent videos. We strive to bridge this gap, and we introduce a zero-shot approach that produces temporally consistent videos of animated characters and requires no training or fine-tuning. We leverage existing text-based motion diffusion models to generate diverse motions that we utilize to guide a T2I model. To achieve temporal consistency, we introduce the Spatial Latent Alignment module that exploits cross-frame dense correspondences that we compute to align the latents of the video frames. Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in a direction that minimizes visual discrepancies. Our proposed approach generates temporally consistent videos with diverse motions and styles, outperforming existing zero-shot T2V approaches in terms of pixel-wise consistency and user preference.

The recent and rapid development of powerful machine learning models for computer vision has boosted 2D and 3D human pose estimation performance from RGB cameras, LiDAR, and radar inputs. These approaches however can require expensive and power-hungry hardware and have raised privacy concerns regarding their deployment in non-public areas. A Carnegie Mellon University research team addresses these issues in the new paper DensePose From WiFi, proposing WiFi-based DensePose, a neural network architecture that uses only WiFi signals for human dense pose estimation in scenarios with occlusion and multiple people. The researchers believe their work could have practical applications in monitoring the well-being of elderly people or identifying suspicious behaviours in the home. DensePose was introduced in 2018 and aims to map human pixels in an RGB image to the 3D surface of the human body.

Imagine a world where you open an apparel application on your phone, tap on clothes you like, and the app shows images of you with those clothes on. On the contrary, we are very close to seeing this kind of technology turning into a real-life application. Currently, data scientists are able to annotate images, but the existing approaches locate a sparse set of joints, like the wrists or elbows, which are often used for applications like gesture or action recognition. Facebook's AI Research division (FAIR) has taken this technique to another level altogether. In order to map all human pixels in 2D images to a 3D surface-based model of the body, they have pioneered a new approach called DensePose.

If you thought DeepFakes, the AI that swaps celebrity faces into any video (like porn), was scary wait until you see what Facebook's DensePose can do. Facebook's AI research (FAIR) division last week revealed the details of a neural network that maps 2D images to humans in videos. Basically the team taught AI how to add "skins" to people in videos – in real-time. If you've ever wanted to live in a world where, at the push of a button, you could turn all the people in any video into a Wookie (for example) this is fabulous news for you. While there have been other 2D image-mapping neural networks, this one is the first to put it all together in real-time and effectively "connect the dots" without a depth sensor.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)