QtMeshEditor-faceblendshapes-onnx is an ONNX model that converts facial images into ARKit-compatible blendshape scores, enabling facial animation and AR applications. Below are 6 other ai apps with similar functionality to QtMeshEditor Faceblendshapes, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
QtMeshEditor Blazeface is an ONNX-format implementation of Google MediaPipe's BlazeFace short-range face detector. The model is designed to detect faces within images, providing outputs that include bounding box coordinates and six keypoints per anchor. It processes input images of size [1,128,128,3] in RGB format, normalized to the range [-1,1], with aspect ratio preserved through letterboxing using zero borders. The model outputs two primary tensors: regressors, which provide box and keypoint data for each anchor, and classificators, which yield score logits for detected faces. 3. The detected face region of interest, determined from eye keypoints and expanded to a square region, is intended for further processing by a face mesh landmark model. 0 license. The ONNX graph is a direct conversion of the original Google MediaPipe model, including both code and weights under the same license. The conversion process and its numerical parity with the Python MediaPipe reference implementation are documented and verified through provided scripts and documentation. evidence_sufficient": true}
QtMeshEditor-facemesh-onnx is an open-source ONNX model for detecting detailed face mesh landmarks in images. It is intended for integration into computer vision pipelines and supports applications like facial morphing and motion capture.
QtMeshEditor-blazepose-onnx is an ONNX model for real-time human pose detection, converted from Google's BlazePose architecture. It allows developers to integrate pose estimation into their applications for research, animation, or fitness tracking purposes.
QtMeshEditor-poselandmarks-onnx is an open-source ONNX model for detecting human pose landmarks in images. It is designed for integration into computer vision pipelines, supporting body capture and motion analysis. The model is suitable for developers building pose estimation or motion capture tools.
QtMeshEditor-mesh-segmentation-building is an open-source ONNX model designed for part segmentation of building meshes in 3D editors. It labels mesh points as wall, roof, window, door, chimney, or foundation, supporting local inference for graphics developers and researchers working with architectural 3D data.
QtMeshEditor Mesh Segmentation Vegetation is a point-cloud part-segmentation model designed to label individual points in 3D meshes of trees and plants. The model follows a PointNet++-style architecture and assigns each point in a vegetation mesh to one of several categories: trunk, branch, foliage, root, or flower (fruit). It is exported to the ONNX format, enabling local inference using ONNX Runtime. This model is part of a suite of category-specialized segmentation models developed for QtMeshEditor, a free and open-source 3D mesh and animation editor. Within the QtMeshEditor application, a companion point-cloud classifier first detects the category of the input mesh. If the mesh is identified as vegetation, the application dispatches it to this segmentation model for detailed part labeling. The broader suite also includes models for other mesh types, such as characters, vehicles, and buildings, but this particular model is dedicated to vegetation segmentation. The model is distributed under the Creative Commons Attribution 4.0 International (cc-by-4.0) license. Its ONNX file is available as part of the QtMeshEditor-models aggregate download source. The tool is intended for use cases requiring automated identification of plant parts in 3D point clouds, such as in mesh editing or animation workflows. No specific integrations, platforms beyond ONNX Runtime, or pricing details beyond the open-source license are mentioned.
