Recovering expressive humans from images is essential for understanding human
behavior. Methods that estimate 3D bodies, faces, or hands have progressed
significantly, yet separately. Face methods recover accurate 3D shape and
geometric details, but need a tight crop and struggle with extreme views and
low resolution. Whole-body methods are robust to a wide range of poses and
resolutions, but provide only a rough 3D face shape without details like
wrinkles. To get the best of both worlds, we introduce PIXIE, which produces
animatable, whole-body 3D avatars with realistic facial detail, from a single
image. For this, PIXIE uses two key observations. First, existing work combines
independent estimates from body, face, and hand experts, by trusting them
equally. PIXIE introduces a novel moderator that merges the features of the
experts, weighted by their confidence. All part experts can contribute to the
whole, using SMPL-X's shared shape space across all body parts. Second, human
shape is highly correlated with gender, but existing work ignores this. We
label training images as male, female, or non-binary, and train PIXIE to infer
"gendered" 3D body shapes with a novel shape loss. In addition to 3D body pose
and shape parameters, PIXIE estimates expression, illumination, albedo and 3D
facial surface displacements. Quantitative and qualitative evaluation shows
that PIXIE estimates more accurate whole-body shape and detailed face shape
than the state of the art. Models and code are available at
this https URL

通过整合不同算法的结果，使用 SMPL-X 公共空间，以及考虑人类形体和性别的关联关系，PIXIE 可以从单一图像中生成具有逼真面部细节的全身三维动画角色。与现有技术相比，PIXIE 获得更准确的全身形状和细节面部形状。

协同回归使用调节表达身体

Collaborative Regression of Expressive Bodies using Moderation

Detecting digital face manipulation has attracted extensive attention due to
fake media's potential harms to the public. However, recent advances have been
able to reduce the forgery signals to a low magnitude. Decomposition, which
reversibly decomposes an image into several constituent elements, is a
promising way to highlight the hidden forgery details. In this paper, we
consider a face image as the production of the intervention of the underlying
3D geometry and the lighting environment, and decompose it in a computer
graphics view. Specifically, by disentangling the face image into 3D shape,
common texture, identity texture, ambient light, and direct light, we find the
devil lies in the direct light and the identity texture. Based on this
observation, we propose to utilize facial detail, which is the combination of
direct light and identity texture, as the clue to detect the subtle forgery
patterns. Besides, we highlight the manipulated region with a supervised
attention mechanism and introduce a two-stream structure to exploit both face
image and facial detail together as a multi-modality task. Extensive
experiments indicate the effectiveness of the extra features extracted from the
facial detail, and our method achieves the state-of-the-art performance.

本文提出了一种将人脸图像分解为 3D 几何形状、贴图和光照的计算机视图，使用人脸细节结合直接光和身份贴图来检测微妙的篡改模式并实现了最先进的性能表现。