Virtual Try-on (VTON) involves generating images of a person wearing selected
garments. Diffusion-based methods, in particular, can create high-quality
images, but they struggle to maintain the identities of the input garments. We
identified this problem stems from the specifics in the training formulation
for diffusion. To address this, we propose a unique training scheme that limits
the scope in which diffusion is trained. We use a control image that perfectly
aligns with the target image during training. In turn, this accurately
preserves garment details during inference. We demonstrate our method not only
effectively conserves garment details but also allows for layering, styling,
and shoe try-on. Our method runs multi-garment try-on in a single inference
cycle and can support high-quality zoomed-in generations without training in
higher resolutions. Finally, we show our method surpasses prior methods in
accuracy and quality.

提出了一种独特的训练方案来解决扩散的训练公式中维持输入服装身份的问题，并且通过多衣物试穿单次推断循环实现高质量的放大生成。

ACDG-VTON：精准和受限扩散生成虚拟试穿

ACDG-VTON: Accurate and Contained Diffusion Generation for Virtual  Try-On

What role does phenotypic complexity play in the systems-level function of an
embodied agent? The organismal phenotype is a topologically complex structure
that interacts with a genotype, developmental physics, and an informational
environment. Using this observation as inspiration, we utilize a type of
embodied agent that exhibits layered representational capacity: meta-brain
models. Meta-brains are used to demonstrate how phenotypes process information
and exhibit self-regulation from development to maturity. We focus on two
candidate structures that potentially explain this capacity: folding and
layering. As layering and folding can be observed in a host of biological
contexts, they form the basis for our representational investigations. First,
an innate starting point (genomic encoding) is described. The generative output
of this encoding is a differentiation tree, which results in a layered
phenotypic representation. Then we specify a formal meta-brain model of the
gut, which exhibits folding and layering in development in addition to
different degrees of representation of processed information. This organ
topology is retained in maturity, with the potential for additional folding and
representational drift in response to inflammation. Next, we consider
topological remapping using the developmental Braitenberg Vehicle (dBV) as a
toy model. During topological remapping, it is shown that folding of a layered
neural network can introduce a number of distortions to the original model,
some with functional implications. The paper concludes with a discussion on how
the meta-brains method can assist us in the investigation of enactivism,
holism, and cognitive processing in the context of biological simulation.

本文旨在研究表型复杂性在具有 “meta-brain models” 机构的偏离系统功能中所起的作用，通过对生理发育中层次结构处理信息和自我调节现象的观察来指导其研究方向，并通过胃的发育过程以及不同过程中的信息表示来验证了折叠和分层作为多种生物情境中信息表现的基础表现方式，同时也提出了 “异地映射”，将 “meta-brains method” 引入到其生物模拟的认知处理和生态主义研究中。

层，折叠和半神经元信息处理

Layers, Folds, and Semi-Neuronal Information Processing

We present a generative model of images based on layering, in which image
layers are individually generated, then composited from front to back. We are
thus able to factor the appearance of an image into the appearance of
individual objects within the image --- and additionally for each individual
object, we can factor content from pose. Unlike prior work on layered models,
we learn a shape prior for each object/layer, allowing the model to tease out
which object is in front by looking for a consistent shape, without needing
access to motion cues or any labeled data. We show that ordinary stochastic
gradient variational bayes (SGVB), which optimizes our fully differentiable
lower-bound on the log-likelihood, is sufficient to learn an interpretable
representation of images. Finally we present experiments demonstrating the
effectiveness of the model for inferring foreground and background objects in
images.

本文提出了基于层叠的图像生成模型，学习每个物体的形状先验，使用 SGVB 进行学习并应用于前景和背景物体推理。