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Overview: Our AI DevOps pipeline builds a high-fidelity digital twin of sensor data which allows for self-improvement of deployed models. We leverage our foundational vision-language models to automatically determine issues in currently deployed AI, pseudo-label or simulate training data, develop models with continual learning and use an LLM-based verification over diverse scenarios.
Overview: Our simulation framework utilizes advances in neural rendering, diffusion models and large language models to automatically transform drive data into a full 3D sensor simulation testbed with unmatched photorealism.
Overview: Our face recognition methods achieve high accuracy on competitive public benchmarks through the use of universal representation learning techniques that leverage very large-scale datasets, with robustness to variations such as occlusions, blur, lighting or accessories.
The recent advent of large-scale 3D data, e.g. Objaverse, has led to impressive progress in training pose-conditioned diffusion models for novel view synthesis. However, due to the synthetic nature of such 3D data, their performance drops significantly when applied to real-world images. This paper consolidates
Photorealistic simulation plays a crucial role in applications such as autonomous driving, where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However, reconstruction quality suffers on street scenes due to largely collinear
We address the challenging task of occlusion-aware indoor 3D scene understanding. We represent scenes by a set of planes, where each one is defined by its normal, offset and two masks outlining (i) the extent of the visible part and (ii) the full region that consists of both visible and occluded parts
