Although neural rendering has made significant ad-vances in creating lifelike, animatable full-body and head avatars, incorporating detailed expressions into full-body avatars remains largely unexplored. We present DEGAS, the first 3D Gaussian Splatting (3DGS)-based modeling method for full-body avatars with rich facial expressions. Trained on multiview videos of a given subject, our method learns a conditional variational autoencoder that takes both the body motion and facial expression as driving signals to generate Gaussian maps in the UV layout. To drive the facial expressions, instead of the commonly used 3D Mor-phable Models (3DMMs) in 3D head avatars, we propose to adopt the expression latent space trained solely on 2D portrait images, bridging the gap between 2D talking faces and 3D avatars. Leveraging the rendering capability of 3DGS and the rich expressiveness of the expression latent space, the learned avatars can be reenacted to reproduce photo-realistic rendering images with subtle and accurate facial expressions. Experiments on an existing dataset and our newly proposed dataset offull-body talking avatars demonstrate the efficacy of our method. We also propose an audio-driven extension of our method with the help of 2D talking faces, opening new possibilities for interactive AI agents. Project page: https://initialneil.github.io/DEGAS.

We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting em-bedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appearance of a virtual human with explicit mesh geometry and implicit appearance modeling with Gaus-sian Splatting. The Gaussians are defined by barycentric coordinates and displacement on a triangle mesh as Phong surfaces. We extend lifted optimization to simultaneously op-timize the parameters of the Gaussians while walking on the triangle mesh. SplattingAvatar is a hybrid representation of virtual humans where the mesh represents low-frequency motion and surface deformation, while the Gaussians take over the high-frequency geometry and detailed appearance. Un-like existing deformation methods that rely on an MLP-based linear blend skinning (LBS) field for motion, we control the rotation and translation of the Gaussians directly by mesh, which empowers its compatibility with various animation techniques, e.g., skeletal animation, blend shapes, and mesh editing. Trainable from monocular videos for both full-body and head avatars, SplattingAvatar shows state-of-the-art ren-dering quality across multiple datasets. Code and data are available at https://github.com/initialneil/SplattingAvatar.

Recovering photorealistic and drivable full-body avatars is crucial for numerous applications, including virtual reality, 3D games, and tele-presence. Most methods, whether reconstruction or generation, require large numbers of human motion sequences and corresponding textured meshes. To easily learn a drivable avatar, a reasonable parametric body model with unified topology is paramount. However, existing human body datasets either have images or textured models and lack parametric models which fit clothes well. We propose a new parametric model SMPLX-Lite-D, which can fit detailed geometry of the scanned mesh while maintaining stable geometry in the face, hand and foot regions. We present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with multi-view RGB sequences, keypoints annotations, textured scanned meshes, and textured SMPLX-Lite-D models. With the SMPLX-Lite dataset, we train a conditional variational autoencoder model that takes human pose and facial keypoints as input, and generates a photorealistic drivable human avatar.

Current deep-learning-based stereo matching algorithms achieve remarkably low error rates but they suffer from the edge ambiguity effect. The primary reason is that they treat disparity estimation as a labeling problem, constructing a cost volume based on uniform discrete pixel-wise labels. It is insufficient to model the continuous disparity probability distribution (DPD), which harms the accuracy of complex regions. Moreover, current cost aggregation strategies cannot process unstructured disparity candidates very well, which is one of the bottlenecks limiting continuous modeling. We propose Patchmatch Stereo++, inspired by the traditional Patchmatch Stereo to achieve better continuous disparity optimization in deep-learning-based methods. Firstly, to model accurate continuous DPD, we introduce an adaptive dense sub-pixel sampling strategy to binocular stereo and approximate a continuous unstructured DPD for every pixel. Secondly, we design a convolution-based optimizer that can accept unstructured disparity candidates to parse the above continuous DPD in an adaptive manner and perform updates accordingly. Extensive experiments demonstrate our method has the best performance among existing stereo matching networks at the edges, both quantitatively and qualitatively. At the time of submission, compared with published works pre-trained on SceneFlow, we rank 1st in the foreground of KITTI and 2nd on SceneFlow, ETH3D under various metrics.The source code will be released.