Mesh

Text2Mesh produces color and geometric details over a variety of source meshes, driven by a target text prompt. Our stylization results coherently blend unique and ostensibly unrelated combinations of text, capturing both global semantics and part-aware attributes. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term neural style field network. In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization.

Ambient Mesh is a next-generation, sidecar-less service mesh designed to simplify security, connectivity, and observability for cloud-native workloads. It enables teams to secure and connect applications without modifying application code or adding operational overhead. Ambient Mesh provides zero-trust, SPIFFE-based security with end-to-end workload encryption. Built-in observability tools deliver distributed tracing, logs, and real-time performance insights. The platform supports advanced traffic control features such as routing, failover, and blue-green deployments. Ambient Mesh allows organizations to migrate from traditional sidecar-based meshes with zero downtime. By reducing complexity and resource usage, it helps teams operate more efficiently at scale.