**Animatable Reconstruction of Clothed Humans** is an end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features.

**CTAL** is a pre-training framework for strong audio-and-language representations with a [Transformer](https://paperswithcode.com/method/transformer), which aims to learn the intra-modality and inter-modalities connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs: masked language modeling and masked cross-modal acoustic modeling. The pre-trained model is a Transformer for Audio and Language, i.e., CTAL, which consists of two modules, a language stream encoding module which adapts word as input element, and a text-referred audio stream encoder module which accepts both frame-level Mel-spectrograms and token-level output embeddings from the language stream

CTAL

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

ARCH

ARCH: Animatable Reconstruction of Clothed Humans

**ALAE**, or **Adversarial Latent Autoencoder**, is a type of autoencoder that attempts to overcome some of the limitations of[ generative adversarial networks](https://paperswithcode.com/paper/generative-adversarial-networks). The architecture allows the latent distribution to be learned from data to address entanglement (A). The output data distribution is learned with an adversarial strategy (B). Thus, we retain the generative properties of GANs, as well as the ability to build on the recent advances in this area. For instance, we can include independent sources of stochasticity, which have proven essential for generating image details, or can leverage recent improvements on GAN loss functions, regularization, and hyperparameters tuning. Finally, to implement (A) and (B), AE reciprocity is imposed in the latent space (C). Therefore, we can avoid using reconstruction losses based on simple $\mathcal{l}\){2}$ norm that operates in data space, where they are often suboptimal, like for the image space. Since it works on the latent space, rather than autoencoding the data space, the approach is named Adversarial Latent Autoencoder (ALAE).

Source	ARCH: Animatable Reconstruction of Clothed Humans
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com