Details in mesh animations are difficult to generate but they have great impact on visual quality. In this work, we demonstrate a practical software system for capturing such details from multi-view video recordings. Given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer, our system captures the motion of both the skeleton and the shape. The output mesh animation is enhanced with the details observed in the image silhouettes. For example, a performance in casual loose-fitting clothes will generate mesh animations with flowing garment motions. We accomplish this with a fast pose tracking method followed by nonrigid deformation of the template to fit the silhouettes. The entire process takes less than sixteen seconds per frame and requires no markers or texture cues. Captured meshes are in full correspondence making them readily usable for editing operations including texturing, deformation transfer, and deformation model learning.

We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed onthe-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results. nition network [25] into identity parameters for the Basel 2017 Morphable Face Model [8].to-image autoencoder with a fixed, morphable-model-based decoder and an image-based loss [28]. This paper presents a method for training a regression network that removes both the need for supervised training data and the reliance on inverse rendering to reproduce image pixels. Instead, the network learns to minimize a loss based on the facial identity features produced by a face recognition network such as VGG-Face [17] or Google's FaceNet [25]. These features are robust to pose, expression, lighting, and even non-photorealistic inputs. We exploit this 1 arXiv:1806.06098v1 [cs.CV]

Figure 1: Face Transfer with multilinear models gives animators decoupled control over facial attributes such as identity, expression, and viseme. In this example, we combine pose and identity from the first frame, surprised expression from the second, and a viseme (mouth articulation for a sound midway between "oo" and "ee") from the third. The resulting composite is blended back into the original frame. AbstractFace Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target-the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities × expressions × visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.

Figure 1. This paper introduces Deep Structured Implicit Functions, a 3D shape representation that decomposes an input shape (mesh on left in every triplet) into a structured set of shape elements (colored ellipses on right) whose contributions to an implicit surface reconstruction (middle) are represented by latent vectors decoded by a deep network. AbstractThe goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Deep Structural Implicit Functions (DSIF), a 3D shape representation that decomposes space into a structured set of local deep implicit functions. We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy (F-Score) than the state-of-the-art (OccNet), while requiring fewer than 1% of the network parameters. Experiments on posed depth image completion and generalization to unseen classes show 15.8 and 17.8 point improvements over the state-of-the-art, while producing a structured 3D representation for each input with consistency across diverse shape collections.

Figure 1: Our system rapidly acquires images under varying illumination in order to compute photometric normals from multiple viewpoints. The normals are then used to reconstruct detailed mesh sequences of dynamic shapes such as human performers. AbstractWe describe a system for high-resolution capture of moving 3D geometry, beginning with dynamic normal maps from multiple views. The normal maps are captured using active shape-from-shading (photometric stereo), with a large lighting dome providing a series of novel spherical lighting configurations. To compensate for low-frequency deformation, we perform multi-view matching and thin-plate spline deformation on the initial surfaces obtained by integrating the normal maps. Next, the corrected meshes are merged into a single mesh using a volumetric method. The final output is a set of meshes, which were impossible to produce with previous methods. The meshes exhibit details on the order of a few millimeters, and represent the performance over human-size working volumes at a temporal resolution of 60Hz.